Role 

We are looking for an experienced Staff Machine Learning Engineer to join our Engineering team. This role is based in Bangalore, reporting to the Senior Engg. Manager - Data + AI.

You will be responsible for designing and developing large scale ML Systems that analyze massive volumes of network data to detect anomalies, threats, and patterns in near real-time. You’ll solve complex real-world business problems by partnering closely with data architects and product teams to deliver impactful automation and intelligence, including the design and orchestration of specialized autonomous agents.

What you’ll do (Role Expectations)

• Build and maintain large-scale distributed systems to support the full ML lifecycle, including data collection, feature engineering, and Agentic AI components like agent workflows and real-time serving
• Solve complex business problems such as threat detection by collaborating with data architects and product teams to build multi-agent solutions that automate analysis and decisioning
• Partner with cross-functional teams to assess requirements, manage scheduling, and lead the development and debugging of agent-based systems
• Support the rollout and monitoring plans for production-grade ML systems to ensure performance, observability, and scalability

Who You Are (Success Profile)

• You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
• You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
• You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
• You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

What We’re Looking for (Minimum Qualifications)

• 5+ years of experience as an AI-ML Engineer with technical proficiency in Java, Springboot, Python, and PyTorch or TensorFlow
• Proven experience writing high-performance queries for large-scale analytics platforms such as Azure Data Explorer (ADX/Kusto) or Google BigQuery, including partitioning-aware patterns
• Deep expertise in designing and deploying anomaly detection using tree-based models, statistical techniques, and streaming ML approaches
• Hands-on experience developing multi-agent orchestration, agent-to-agent communications, and autonomous decision-making workflows
• Strong technical background with distributed infrastructure and public cloud platforms like Azure, GCP, or AWS

What Will Make You Stand Out (Preferred Qualifications)

• A track record of shipping end-to-end applications at scale that process billions of events
• Willingness to work across the full spectrum of Big Data and AI-based solutions
• Hands-on experience with SOTA AI frameworks, prompt engineering, and complex agent architectures

#LI-Bangalore #LI-AN4

As a Senior AI/ML Engineer in our AdTech team, you will be a hands-on individual contributor building and deploying machine learning models and AI-driven features for our advertising platform. You will partner with engineering, product, and data science to deliver production-grade ML for campaign optimization, user personalization, and creative intelligence—operating at large scale and low latency across billions of ad events per day. You will contribute to modern ML/LLM capabilities and agentic workflows that automate and enhance campaign operations, with a strong focus on reliability, performance, and measurable business impact.

Key Responsibilities

• Machine Learning Delivery: Design, implement, and ship scalable ML solutions for core AdTech use cases (targeting, ranking, pacing, measurement). Own features end-to-end from experimentation to production rollout.
• ML System Design: Build and evolve the ML lifecycle—data preparation, training, evaluation, and real-time inference—ensuring models integrate cleanly with ad serving systems and meet low-latency, high-throughput requirements.
• Technical Contribution: Contribute to the AI/ML technical roadmap by evaluating tools and techniques (including deep learning, LLMs, and retrieval/feature systems). Make pragmatic trade-offs with an eye toward maintainability and operational excellence.
• AI & Agentic Applications: Develop and integrate LLM-powered features and agentic workflows that assist with campaign workflows such as audience insights, bid/budget recommendations, and creative generation—within well-defined guardrails.
• Cross-Functional Collaboration: Work closely with engineering, product, and data science partners to translate marketing objectives into ML-driven solutions, define success metrics, and deliver iterative improvements.
• Performance & Reliability: Operate ML services in a high-concurrency, latency-sensitive environment. Optimize inference paths, implement monitoring/alerting, manage model drift, and ensure safe deployment practices (A/B testing, canaries, rollbacks).
• Mentorship & Best Practices: Mentor and support other engineers through code/model reviews and knowledge sharing. Champion engineering rigor in testing, observability, documentation, and reproducible ML workflows.

Required Qualifications

• 5+ years of experience in software engineering and/or applied machine learning, with a track record of shipping ML systems to production.
• Experience designing and building high-throughput, low-latency services or data pipelines for large-scale applications.
• Working knowledge of the programmatic advertising ecosystem (DSP/SSP/RTB) is a plus; strong adjacent experience (recommendation, ranking, marketplaces) is also valued.
• Proficiency in at least one of Java, Go, or Python for backend services and ML tooling.
• Hands-on experience with ML frameworks (PyTorch or TensorFlow) and common modeling approaches (classification, ranking, embeddings, deep learning).
• Experience with big data and streaming frameworks (e.g., Spark, Kafka) for processing and analyzing large datasets.
• Experience deploying ML to cloud environments (preferably AWS) and operating services at scale.
• Familiarity with data stores (SQL/NoSQL) such as PostgreSQL/MySQL, Cassandra/DynamoDB, Redis, etc.
• Familiarity with containerization/orchestration (Docker, Kubernetes) and CI/CD practices.
• Strong communication skills and ability to collaborate across disciplines; comfort explaining technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with Large Language Models (LLMs) and generative AI applied to advertising (e.g., ad copy generation, creative optimization, or personalized messaging).
• Experience designing and implementing agentic workflows that support autonomous or semi-autonomous decision-making with strong safety/observability guardrails.
• Experience with model serving and optimization for real-time inference (latency-critical environments).
• Familiarity with modern data lake and table formats (e.g., Apache Iceberg, Apache Hudi) for managing large-scale analytical datasets.
• Knowledge of microservices architecture and event-driven design patterns in distributed systems.
• Contributions to open-source projects, publications, or speaking engagements that demonstrate thought leadership in AI/ML are a plus.

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

The Role:

• As an Applied Value Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

The work you’ll do:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Solution Engineer stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Solution Engineers specialize in domains (e.g. supply chain) and industries

The qualifications you need:

• 3-5 years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective.

You will report to our Director, Analytics Engineering & Data Governance and work from our Bangalore, India office. 

You Will:

Model and Pipeline Automation 

• Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices. 
• Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models.
• Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models.

Governance & Compliance

• Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards.

Performance Monitoring

• Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation. 
• Incorporate user feedback for iterative improvements and manage necessary model retraining cycles.

Cross-Functional Collaboration

• Act as the "glue" between Data Scientists (who build models) and Software Engineers (who consume them).
• Partner effectively with software engineers, product managers and business functions to integrate the machine learning solutions across smartsheet.

Architecture and Infrastructure Management

• Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC).
• Provide architectural guidance and mentorship to a team consisting of ML engineers, data scientists and analytics engineers.
• Distill complex ML concepts into easy-to-follow technical documentation.

You Have:

• 5+ years of experience with creating, deploying and scaling machine learning solutions in a cloud environment (eg. AWS, GCP, Azure) and ability to use tools such as SageMaker, Glue, Lambda, Docker etc. to create ML models and data pipelines. 
• 7+ years of programming experience in languages used in AI/ML (eg python, scala etc)
• 4+ years of experience in developing deep learning and traditional ML models using common frameworks like pytorch, tensorflow, huggingface, scikit-learn etc.
• Strong applied data science skills - ability to recognize data patterns, understand how and when to use various machine learning approaches (eg. supervised/unsupervised learning, deep learning etc.), and evaluate the performance of ML algorithms. 
• Proven ability to remain up-to-date with the latest advancements in Generative AI approaches (eg. OpenAI, LangChain, Stable Diffusion APIs).
• Experience developing, documenting, and supporting REST APIs
• A degree in Computer Science, Engineering, or a related field or equivalent practical experience.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.
 

As Senior Analyst in our Model Risk Management team, you will help manage the Model Risk Program including completing model validation reviews, maintaining the model risk inventory, and monitoring model performance particularly for Fraud and Gen AI Models. You will work closely with the Data Science Team, architects, engineers and product managers to assess the risk of model design, implementation, and use across product lines.

A day in the life (Responsibilities) 

• Support the implementation and day-to-day execution of the Second Line of Defense (2LOD) Model Risk Management (MRM) program for high-risk models, with particular focus on Fraud detection models (Transaction Fraud & Merchant Fraud) and Generative AI / LLM-based systems deployed across Toast.
• Assist in maintaining and enhancing the Model Risk Management framework, including policies, procedures, validation standards, governance documentation, templates, and best practices aligned with evolving regulatory and industry expectations.
• Enforce model lifecycle standards across development, implementation, use, monitoring, recalibration, change management, governance, and decommissioning, ensuring appropriate controls for traditional ML models as well as GenAI systems (e.g., RAG architectures, copilots, AI-assisted decision tools).
• Contribute to the development, risk-tiering, and ongoing maintenance of a comprehensive model inventory, including assessment of model impact, intrinsic risk (complexity and methodology), reliance on model outputs, and emerging AI-specific risks.
• Perform independent model validation reviews under the guidance of senior leadership, covering conceptual soundness, data integrity, model methodology, performance metrics (e.g., AUC, precision/recall, calibration), stability, bias/fairness risk, explainability, and monitoring frameworks. Produce validation reports and track issue remediation plans through closure.
• For Fraud models, evaluate class imbalance handling, threshold optimization, cost-sensitive performance metrics, operational overlays, rule-based controls, and portfolio-level impact analyses.
• For Generative AI systems, validate systems and evaluate risks related to hallucination, prompt injection, adversarial vulnerabilities, data privacy and leakage, model explainability limitations, bias, guardrails, output monitoring, jailbreak testing, regression testing, and human-in-the-loop controls.
• Partner with Data Science, Data Engineering, Product/Engineering, Information Security, Legal/Compliance, Finance, Credit Risk and Business teams to obtain documentation, perform effective challenge, conduct validation and oversee performance monitoring
• Prepare reports and executive materials summarizing model risk issues, validation findings, monitoring insights, and remediation status for leadership review, risk committees, audit committees, and internal audit engagements.
• Research and stay informed on industry developments in fraud analytics, machine learning, Generative AI governance, and regulatory guidance (e.g., SR 11-7, OCC 2011-12, NIST AI RMF). Propose enhancements to strengthen Toast’s Model Risk and AI Governance framework.
• Contribute to the development of model risk training materials and support delivery of training sessions to key stakeholders to enhance awareness of fraud model risk and AI-related risks.
• Support ad-hoc initiatives related to model risk governance, AI oversight, regulatory compliance, and enterprise risk management enhancements.

What you'll need to thrive (Requirements)

• Advanced degree in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, or a related quantitative discipline
• 5+ years of relevant industry experience in Model Risk Management, Model Validation, Data Science, or Machine Learning within fintech, banking, payments, technology, or consulting environments, with exposure to fraud analytics, credit risk, or AI/ML model governance.
• Strong understanding of machine learning methodologies and statistical foundations, including model development, validation techniques, performance evaluation, calibration, and stability analysis.
• Hands-on experience with fraud detection models (e.g., transaction monitoring, chargeback prediction, merchant risk scoring) and familiarity with concepts such as class imbalance handling, threshold optimization, precision/recall trade-offs, and cost-sensitive evaluation.
• Familiarity with Generative AI and LLM-based systems (e.g., RAG architectures, embeddings, prompt engineering, AI copilots) and awareness of associated risks such as hallucination, bias, prompt injection, and data leakage.
• Strong proficiency in Python and SQL; experience with data science and ML tools such as Spark, scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Familiarity with software engineering best practices (version control, CI/CD, testing frameworks, Airflow, AWS or cloud platforms) is a plus.
• Knowledge of model risk management frameworks and regulatory guidance (e.g., SR 11-7, OCC 2011-12) and familiarity with emerging AI governance frameworks (e.g., NIST AI RMF) is highly desirable.
• Ability to translate complex quantitative methodologies into clear business insights and effectively challenge first-line model development teams while maintaining strong cross-functional partnerships.
• Demonstrated critical thinking, analytical rigor, and structured problem-solving skills, with the ability to manage multiple priorities and deliver high-quality outputs under tight deadlines.
• Excellent written and verbal communication skills, including experience drafting formal validation reports, executive summaries, and committee-level materials.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy

We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems
  
  
  

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

At Reltio®, we believe data should fuel business success. Reltio’s AI-powered data unification and management capabilities—encompassing entity resolution, multi-domain master data management (MDM), and data products—transform siloed data from disparate sources into unified, trusted, and interoperable data. Reltio Data Cloud™ delivers interoperable data where and when it's needed, empowering data and analytics leaders with unparalleled business responsiveness. Leading enterprise brands—across multiple industries around the globe—rely on our award-winning data unification and cloud-native MDM capabilities to improve efficiency, manage risk and drive growth.

At Reltio, our values guide everything we do. With an unyielding commitment to prioritizing our “Customer First”, we strive to ensure their success. We embrace our differences and are “Better Together” as One Reltio. We are always looking to “Simplify and Share” our knowledge when we collaborate to remove obstacles for each other. We hold ourselves accountable for our actions and outcomes and strive for excellence. We “Own It”. Every day, we innovate and evolve, so that today is “Always Better Than Yesterday”. If you share and embody these values, we invite you to join our team at Reltio and contribute to our mission of excellence.

Reltio has earned numerous awards and top rankings for our technology, our culture and our people. Reltio was founded on a distributed workforce and offers flexible work arrangements to help our people manage their personal and professional lives. If you’re ready to work on unrivaled technology where your desire to be part of a collaborative team is met with a laser-focused mission to enable digital transformation with connected data, let’s talk!

About the Role

We are seeking a Senior AI Engineer with a strong foundation in Python capabilities, and hands-on experience building Agentic AI applications that integrate with Large Language Models (LLMs) Large Language Models (LLMs). We are also looking for him to be a full-stack Engineer with hands-on experience with mobile front-end hands-on experience with mobile front-end Flutter, React-native. This role blends traditional backend engineering expertise with next-generation AI agentic systems development, requiring not just system design skills, but also creativity in prompt engineering, AI product development and working with data-rich environments.

You will contribute to the design and implementation of intelligent backend services that coordinate LLMs, agents, and data workflows to support enterprise-grade agentic systems.

Key Responsibilities

• Develop robust, scalable Java/Python based backend systems that enable the orchestration of Agentic AI workflows.
• Develop state-of-the-art AI/ML models for solving real-world data management challenges such as entity resolution, classification, similarity matching, and anomaly detection.
• Integrate LLMs (e.g., OpenAI, Mistral, Claude, LLaMA) into backend pipelines to power autonomous or semi-autonomous decision-making.
• Hands on experience with mobile front-end Flutter, React-native etc.
• Develop scalable ML workflows using Spark, MLlib, PyTorch, TensorFlow, or MLFlow, with seamless integration into production systems.
• Translate business needs into technical design and collaborate with data scientists, product managers, and platform engineers to operationalize models.
• Work closely with various stakeholders to design agent architectures and LLM toolchains (using frameworks like LangGraph, Crew AI, AutoGen, etc.).
• Implement services and APIs that manage prompt chaining, tool invocation, and contextual memory for agents.
• Collaborate with data engineers to ensure clean, efficient, and real-time access to structured and semi-structured data.
• Apply prompt engineering best practices to improve agent behavior, task accuracy, and adaptability.
• Monitor and optimize service performance, reliability, and scalability in production environments.
• Continuously monitor and improve model performance using feedback loops, A/B testing, drift detection, and retraining strategies.
• Contribute to code reviews, mentoring, and best practices for hybrid backend/AI development.

Required Qualifications

• 5+ years of overall experience mobile front-end Flutter, React-native etc
• 2+ years of experience in developing and deploying machine learning models and AI products in production environments.
• Experience in Agentic AI systems development using tools such as LangGraph, Crew AI, AutoGen, or similar.
• Proficiency in Python (NumPy, scikit-learn, pandas, PyTorch/TensorFlow) and experience with large-scale data processing tools (Spark, Kafka, Airflow).
• Experience integrating or orchestrating LLMs within business workflows or intelligent assistants.
• Solid understanding of prompt engineering techniques (e.g., system messages, chaining, tool invocation prompts).
• Familiarity with data engineering concepts, including pipelines, data models, and ETL workflows.
• Strong grasp of object-oriented design, data structures, and algorithms.

Preferred Qualifications

• Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, RAG pipelines).
• Background in building microservices or backend systems for data-rich AI/ML platforms.
• Experience with vector databases, embedding services, or semantic search integration.
• Knowledge of cloud platforms (AWS, GCP, or Azure), containerization (Docker), and DevOps best practices.
• Experience in developing LLM-powered agents, copilots, or task bots.

What We Offer

• Opportunity to work on cutting-edge backend and AI systems, including agentic automation and LLM innovation.
• A flexible hybrid work model in our Bangalore office.
• Competitive compensation with performance-based rewards.
• A culture that promotes engineering excellence, experimentation, and growth.Access to learning platforms, technical workshops, and industry-leading AI research.

Why Join Reltio?*

• Health & Wellness:
  • Comprehensive Group medical insurance including your parents with additional top-up options.
  • Accidental Insurance
  • Life insurance
  • Free online unlimited doctor consultations
  • An Employee Assistance Program (EAP)
• Work-Life Balance:
  • 36 annual leaves, which includes Sick leaves – 18, Earned Leaves - 18
  • 26 weeks of maternity leave, 15 days of paternity leave
  • Very unique to Reltio - 01 week of additional off as recharge week every year globally
• Support for home office setup:
  • Home office setup allowance.
• Stay Connected, Work Flexibly: Mobile & Internet Reimbursement
• No need to pack a lunch—we’ve got you covered with a free meal.And many more…..

Role Description

• As a Senior Applied Value Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Senior Applied Value Engineer stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Senior Applied Value Engineer specialize in domains (e.g. supply chain) and industries

Requirements:

• 6+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

Role Description

• As a Senior Applied Value Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Senior Applied Value Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 6+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

The Team:

The Celonis Value Engineering (VE) team is focused on managing the entire customer value lifecycle, from sales through value realization, renewal and expansion. As a Value Engineer - India, your primary focus will be supporting our GCC's (Global Competency Centers) across the Adopt and Expand phases of our customer value journey. This team will be a part of the Global VE CoE spearheading our mission of data-driven business transformation with our customers.You will blend deep process and industry expertise with a proven consulting skill set with strong analytical skills at the forefront of one of the fastest-growing tech companies worldwide.

The Role:

As an Applied AI Value Engineer you are pushing the envelope in solving mission-critical problems for our customers. You will be working with our most strategic customers, understanding their strategy and key challenges, and building Celonis solutions using cutting edge AI technologies of market leaders such as Microsoft and OpenAI. With Celonis’ market leading Process Intelligence (PI) Platform we feed operational context to AI so it understands our customers’ businesses and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

The work you’ll do:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

The qualifications you need:

Requirements:

• 4+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

The Role:

As an Applied Value Engineer for the Japan Market, you will solve business-critical problems for our most strategic Japanese customers. You will bridge the gap between cutting-edge AI technology and the unique business requirements of Japanese enterprises.
Working with the world’s leading Process Intelligence (PI) platform and partners like Microsoft, OpenAI, and Databricks, you will feed operational context to AI to help Japanese organizations industrialize AI and unlock real ROI. You will prototype solutions, demonstrate value to executives, and ensure successful implementation and adoption within the Japanese business ecosystem

The work you’ll do:

• AI Discovery & Solutioning (Japan Focus): Understand the specific AI strategies and challenges of Japanese customers. Translate their requirements into innovative solutions that align with local business practices.
• Hackathons & Prototyping: Rapidly build creative prototypes (LLM/Agent-based) to solve business-critical problems during customer hackathons.
• Agentic Process Transformation: Enable a fundamental shift for our customers from traditional automation to autonomous AI agents empowered by the Celonis PI Platform.
• Proof Projects: Execute end-to-end Proof-of-Value (PoV) projects, including architecture, RAG, and guardrails, ensuring compliance with enterprise data and identity frameworks.
• Cross-Border Collaboration: Work closely with the Japan-based account teams and executives to ensure project outcomes reach agreed value and adoption thresholds.
• Domain Specialization: Specialize in domains (e.g., Supply Chain, Finance) to scale knowledge across the Japan and global organizations.

The qualifications you need:

• Market Alignment: Strong willingness and commitment to work specifically for the Japan market. This includes flexibility to align with Japan Standard Time (JST) for key meetings and workshops.
• Professional Experience: 3-5 years of experience in technical pre-sales, defining AI roadmaps, and building ROI/TCO business cases.
• Technical Expertise: Deep understanding of Generative AI techniques (RAG, multi-agent orchestration, prompt engineering) and Python (LangChain, pandas, PyTorch, etc.).
• Process Knowledge: Understanding of business processes (Supply Chain, Finance, etc.) and the ability to translate high-level needs into AI use cases.
• Communication: Strong presentation skills to engage with both internal stakeholders and external Japanese enterprise executives.
• Education: Bachelor’s Degree required (Master’s in CS, Engineering, or Math preferred).

• Japan Market Experience: Previous experience working with Japanese companies as clients or partners is highly valued.
• Cultural Competency: Familiarity with Japanese business etiquette and communication styles.
• Hands-on AI implementation: Experience building agentic systems and deploying models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).
• LLM Ecosystem: Working knowledge of LangChain, LlamaIndex, or other OSS packages.

What is the InMobi family like?   

We are an infectious bunch. Be it the way we rise to challenges, the innovative products we create, the dreams we chase or the fun we have at work. We are sure that if you meet us, you will be infected too. Today, we are proud to be the leaders in Mobile advertising and are on an accelerated path of being a leader in enterprise software for marketers. We invite you to free yourself, dream big and chase your passion. We are here today because a few of us did just that.   

What can we promise?   

We offer you an opportunity to work on building enterprise platforms that require you to acquire, hone and demonstrate your ML and AI skills in designing and solving for complex problems, thereby creating software that creates value for our customers in a reliable, scalable, secure, and AI-first manner. At InMobi, you get to work on innovative technologies, work closely with business teams and grow as an overall technology leader.  

Modern work environment, flexible schedule, and smart, creative, down-to-earth people. Internal opportunities to move roles and try out bridge assignments with different teams.  Food for your soul – free meals all days of the week, gym, or yoga class to flex those biceps, cocktails at drink cart Thursdays and fun at work on Funky Fridays. We even promise to let you bring your kids and pets to work.  

What is the team like?  

At InMobi Enterprise team, we are transforming the way finance, sales, marketing, people, and legal functions operate. We’re driving transformative work across Sales, Marketing, People, and Finance with ML and Agentic AI—and we’re looking for passionate innovators to join us!

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, GenAI and modern agentic AI systems with a strong engineering appetite. You will build and deploy intelligent AI/ML systems that will help transform the business process across the core. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

• Lead the experimentation, design and build of agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Support POCs/ Pilots projects to explore and validate innovative technologies and solutions in ML/AI space.

• Build and operate machine learning models on diverse, high-volume data sources for forecasting, classification and prediction.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Design pipelines for data preparation, model training, evaluation, and deployment in collaboration with engineering counterparts.
• Define best practices for monitoring ML model performance using statistical techniques; identifying drifts, failure modes, and improvement opportunities.
• Contribute to ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.
• Document designs, development processes, and best practices to promote knowledge sharing and operational efficiency.

What do we expect from you?

We are seeking candidates with deep expertise in ML and hands-on experience building GenAI and Agentic AI systems.

Skillset: AI/ML Engineering, Python, LLM, RAG, Agentic AI, MCP, REST API

You should have experience with:

• Design, build, and deploy building LLM-powered applications, chatbots, and multi-agent systems to improve efficiency, accuracy, and decision-making.
• Implement and customize GenAI and multi-agent systems using AI frameworks such as LangChain, LangGraph, CrewAI, or similar.
• Create, refine, and maintain prompt templates, chains, and workflows.
• Integrate vector databases (MilvusDB, FAISS, Pinecone, Chroma, etc.) for semantic search and memory.
• Classical ML and deep learning techniques across NLP, clustering, and time series.
• Proficient in deploying ML workflows/models in production system. Continuous monitoring, evaluation, and retraining of models to ensure sustained performance. Ensuring model explainability, accuracy, compliance.
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Stay updated with emerging AI/ML technologies and recommend relevant innovations for the finance domain.
• Drive in design reviews, sprint planning, and technical discussions.
• Collaborating effectively with diverse stakeholders, including technical teams and business leaders.
• Adapting to fast-paced environments and evolving priorities with high energy and autonomy.

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

 Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 8+ years of industry experience in ML/Data Science, with deep proficiency in Python and one or more frameworks: PyTorch, TensorFlow, Scikit-learn.
• Solid understanding of ML fundamentals, regression, tree-based models, clustering, and time series.
• Hands-on experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Experience building Agentic frameworks such as LangGraph, AutoGen, CrewAI, n8n or ReACT-style agents.
• Expertise with algorithms in NLP, Time Series, and Deep Learning, applied on real-world datasets.
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Experience with Model Context Protocols (MCPs) — building or integrating MCP tools, servers, or capabilities.
• Comfortable working in cross-functional teams.
• Strong problem-solving and analytical skills with the ability to work in agile environments.
• Good understanding of data pipelines, APIs, and cloud platforms (Azure/GCP/AWS).
• Excellent communication skills with the ability to simplify complex technical concepts.

What You Will Be Doing

- Design and implement production-ready generative AI applications that serve millions of users, from initial architecture through deployment and monitoring
- Build advanced RAG (Retrieval-Augmented Generation) pipelines that combine vector databases, hybrid search, and intelligent caching to deliver sub-second response times
- Develop multimodal AI systems that seamlessly integrate text, vision, and audio capabilities using state-of-the-art models
- Architect scalable microservices that handle thousands of concurrent AI requests while optimizing for cost, latency, and reliability
- Lead code reviews and technical design sessions, establishing best practices and architectural patterns that elevate the entire team's capabilities
- Optimize large language models through fine-tuning techniques to achieve domain-specific performance improvements
- Implement comprehensive MLOps practices including automated testing, model versioning, A/B testing frameworks, and real-time monitoring dashboards
- Collaborate with product managers and stakeholders to translate complex business requirements into innovative AI solutions
- Deploy AI models across multiple cloud platforms (GCP) using containerization and orchestration technologies
- Create and maintain technical documentation, runbooks, and architectural decision records that enable knowledge sharing across teams
- Mentor junior engineers through pair programming, technical talks, and hands-on guidance to accelerate their growth
- Research and prototype emerging AI technologies to identify opportunities for competitive advantage

Gen AI Responsibilities

- Fine-tune and optimize state-of-the-art language models for specific business use cases, achieving significant improvements in accuracy and relevance
- Design multi-agent AI systems using frameworks to orchestrate complex workflows and decision-making processes
- Implement advanced prompt engineering strategies including Tree of Thoughts, ReAct patterns, and automatic prompt optimization to maximize model performance
- Build production-grade embedding systems that handle billions of vectors, implementing efficient indexing strategies and hybrid search capabilities
- Develop computer vision pipelines using models for tasks ranging from object detection to visual question answering
- Create secure AI applications with robust safeguards against prompt injection, jailbreaking, and data leakage while maintaining compliance with AI governance standards
- Optimize token usage and implement intelligent caching strategies to reduce costs by 50-70% while maintaining quality
- Design and implement evaluation frameworks that go beyond traditional metrics, incorporating human feedback loops and domain-specific quality measures
- Build real-time AI inference systems capable of processing streaming data with sub-100ms latency requirements
- Integrate multiple foundation models into unified applications, implementing fallback mechanisms and load balancing for high availability
- Develop custom tools and functions that extend LLM capabilities, enabling models to interact with databases, APIs, and external systems
- Implement advanced RAG techniques including contextual embeddings, cross-encoder reranking, and Graph RAG for complex reasoning tasks
- Create multimodal search systems that enable users to query across text, images, and documents using natural language
- Build AI-powered data processing pipelines that automatically extract, transform, and enrich unstructured data at scale
- Deploy edge AI solutions using frameworks like ONNX and TensorRT, optimizing models for resource-constrained environments

What We're Looking For

- 5+ years of hands-on experience building and deploying ML/AI systems, with at least 2+ years focused on generative AI and LLMs
- Expert-level Python programming skills with deep knowledge of async programming, multiprocessing, and performance optimization
- Strong experience with modern AI frameworks including PyTorch, Transformers, LangChain, and vector databases
- Proven track record of deploying AI applications to production environments serving real users at scale
- Deep understanding of transformer architectures, attention mechanisms, and the latest advances in generative AI
- Experience with cloud platforms (GCP) and containerization technologies (Docker, Kubernetes)
- Excellent communication skills with the ability to explain complex AI concepts to both technical and non-technical audiences
- Proven experience improving large-scale product search and discovery — including dense retrieval with bi-encoders, cross-encoder reranking, query understanding, and hybrid BM25 + vector search across catalogs of tens of millions of SKUs
- Hands-on experience building and deploying production multi-agent systems using orchestration frameworks such as LangGraph and Google ADK — designing stateful, tool-augmented agents for complex, real-world workflows
- Bachelor's degree in Computer Science, Mathematics, or related field (Master's preferred but not required with relevant experience)

Nice to Have

- Published research papers or significant contributions to open-source AI projects
- Experience with multimodal AI systems combining vision, language, and audio
- Domain expertise in specific verticals (healthcare, finance, legal, e-commerce)
- Knowledge of AI safety, alignment, and constitutional AI principles
- Experience building AI infrastructure and platforms used by other engineers
- Familiarity with emerging technologies like neural architecture search, mixture of experts, or neuromorphic computing

Role Description

• As an Applied Value Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 5+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Experience in Oil & Gas industry is must.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

About the role

We are seeking a Machine Learning Engineer Intern to contribute to the development of computer vision algorithms focused on item authentication and fingerprinting. In this role, you will support the research, development, and deployment of algorithms and models aimed at accurately determining the authenticity of items through the analysis of their unique visual characteristics and assigning unique digital identities based on image data.
You will also assist in refining the image capture experience on edge devices and contribute to ensuring model interpretability.

Reports to: ML Lead
Location: Remote or Hybrid in Bangalore, India
Duration:  6 months internship with an opportunity for PPO

What You'll Do:

• Algorithm Research & Support: Assist in researching, designing, and training computer vision models for authentication, fingerprinting, image capture, and other customer-centric computer vision applications.
• Code Quality & Testing: Write clean, modular, and well-tested code. Develop unit and integration tests to maintain high standards of code quality and reliability. Participate in code reviews, contributing to pull requests and collaborating with team members.
• Model Deployment Support: Provide support in building and maintaining APIs to efficiently deploy models in production.
• Continuous Learning: Stay current with advancements in Computer Vision and Machine Learning.
• Collaboration: Work with internal teams to understand needs and gather feedback on algorithm performance.
• Documentation: Contribute to clear and concise documentation of algorithms and models.

Who you are:

• Currently pursuing a Bachelor's or Master's degree in Electronics, Physics, Computer Science, or a related field.
• Foundational understanding of Computer Vision algorithm development.
• Familiarity with Python.
• Basic understanding of PyTorch and OpenCV.
• Strong foundational skills in probability & statistics, calculus, geometry, optimization, and linear algebra.
• Ability to rapidly acquire new skills and technologies.
• Strong problem-solving skills and attention to detail.
• Willingness to contribute to code reviews and collaborate with peers.

What we offer

• Market-competitive compensation structure
• Opportunities to gain hands-on experience in cutting-edge computer vision solutions.
• A collaborative, mission-driven work culture with direct product impact.
• Mentorship from experienced Machine Learning Engineers.
• Opportunities to learn about deploying models on edge devices.

We have had an incredible run so far and laying the foundation for a culture that is fast-paced, entrepreneurial, and rooted in passion, kindness, and positivity. We live by these values – we hire by them, promote them, and celebrate them every day. 

If you are a passionate and eager individual looking to kickstart your career in Computer Vision and Machine Learning, we'd love to connect!

Job Title: Manager, Software Engineer

Locations: Bangalore

Reports to: VP, AI Engineering

 A quick snapshot…

Conga is looking to hire a Manager, Software Engineering to join our extremely talented AI Engineering team. As an Engineering Manager at Conga, you’ll lead a team of software engineers working on our next generation AI Solutions. This team will be responsible for building statistical and machine learning models that support product features across the company. You’ll get to work directly with other Engineering Managers, Architects, Data Scientists, Product Managers, and Directors, all of whom make up the larger team. In this highly collaborative environment, you will have the opportunity to grow as a leader and help mentor others.

This position requires experience leading software engineering initiatives, knowledge of AI/ML solutions, as well as excellent people and time management skills. This role will be reporting to the VP of AI Engineering and will be a key engineering leader inside the Conga AI Pillar. The role requires you to collaborate with other leaders at Conga and guide your team to deliver high quality software solutions in a timely manner. The ideal candidate for this position is passionate about people, process, technology, and delivering customer values.

Conga works in an agile environment where there are daily stand-ups, code reviews, and constant communication within each self-managed cross-functional team. The ability to communicate effectively with other Engineering and Product leaders, as well as work closely with other your team members are key components for success in this position.

Why it’s a big deal…  

8+ or more years of experience in the Software Engineering industry with a record of increasing responsibility leading software development initiatives, and 3+ years managing teams.

Deep understanding of the Software Development Lifecycle (SDLC) and Machine Learning Lifecycle (MLL).

Lead and drive the strategy, vision and roadmap for building robust, scalable AI/ML foundations and platforms — including but not limited to:

•  Feature stores
• Distributed ML model training
• Model versioning and evaluation
• Monitoring, drift detection, and automated retraining pipelines
• Responsible AI, model explainability, and fairness assessments

Experience with productionizing ML models and integrating them into real-time and batch applications. Work closely with data science partners and domain engineering teams to understand AI/ML use cases and build corresponding foundations for personalization, NLP, forecasting, recommendations, and generative AI.

Experience using a popular programming language such as: Python (preferred), Java, C#, Scala, etc.

Solid understanding of modern software development practices including object-oriented programming, concurrency, design patterns, microservices architecture, RESTful API design, and test-driven development.

Experience with software testing concepts and tools: white/black box testing, coverage analysis, mocking, PyTest/xUnit, Selenium, etc.

Hands-on experience with GCP technologies such as BigQuery, GKE, GCS, DataFlow, Kubeflow, and Vertex AI — or equivalent services in AWS and/or Azure.

Familiarity with ML frameworks and tools such as TensorFlow, PyTorch, MLflow, scikit-learn, HuggingFace Transformers, etc.

Strong experience with Infrastructure-as-Code and CI/CD tools used to automate deployment to cloud platforms: Terraform, GitHub Actions, Concourse, Ansible, etc.

Excellent leadership, decision-making, and communication skills, with the ability to translate business needs into engineering outcomes.

Experience with AI product lifecycle management, including experimentation tracking, model governance, and release management.

Experience with Python is ideal; Java/Spring is a strong plus.

Education: Bachelor’s degree in computer science, Engineering, or a related technical field; Master’s degree preferred 

Are you the person we’re looking for?  

Provide engineering leadership and mentorship to a team of software engineers.

Be a strong engineering leader responsible for executing the Conga AI roadmap using agile practice.

Experience implementing scalable data and AI/ML solutions in a product-driven technology environment.

Manage, lead, and motivate cross-functional, interdisciplinary teams of software engineers to achieve strategic and tactical business outcomes.

Mentor and grow engineers in your team, fostering a culture of teamwork, accountability, and continuous improvement.

Demonstrated ability to skillfully hire, coach, and performance manage a team of technology professionals.

Partner with others at Conga to ensure engineering tasks are understood, broken down, and implemented to the highest of quality standards.

Collaborate with members of the team to solve challenging engineering tasks on time and with high quality.

Encourage skill growth of your team members.

Assist and adhere to enforcement of project deadlines and schedules.

Actively communicate with team members to clarify requirements and overcome obstacles to meet the team goals.

Highly technical, confident at the operational management level and comfortable in an engineering-heavy organization.

Track record of being a strong leader, with the ability to influence across teams and levels.

Work with development teams in an agile context as it relates to software development.

Here’s what will give you an edge…

Provide architectural guidance and technical oversight for AI/ML systems, ensuring solutions are scalable, secure, and production ready.

Drive engineering excellence by championing robust design, peer reviews, and efficient CI/CD practices tailored for AI systems.

Foster strong cross-functional partnerships with Product, Data Science, ML-Ops, and DevOps teams to accelerate delivery and innovation.

Travel Requirement

Limited travel to other company offices upon request

#LI-UB1

Job Title: Staff Software Engineer, AI

Locations: Bangalore

Reports to: Sr. Manager, AI/ML.

A quick snapshot…

As a Staff Software Engineer, you will be a key contributor to the design, development, and deployment of advanced AI and generative AI-based products. You will drive technical innovation, lead complex projects, and collaborate closely with cross-functional teams to deliver high-quality, scalable, and maintainable solutions. This role requires a strong background in software development, AI/ML techniques, and DevOps practices, along with the ability to mentor junior engineers and contribute to strategic technical decisions.

Why it’s a big deal…

This is one of the critical roles in the project, where you will be an expert in product development, a good team player, and will lead and mentor your team members. We believe in using the best tools for the task at hand so the ability and desire to learn new programming languages and technologies is necessary. All of this adds up to an exciting, challenging, and always interesting place to work, where complex problems are found and solved every day. This role determines the root cause for the most complex software issues and develops practical, efficient, and permanent technical solutions.

Are you the person we’re looking for?

Experience: 8+ years of professional software development experience, including significant experience with AI/ML or GenAI applications. Demonstrated expertise in building scalable, production-grade software solutions.

Advanced Software Development: Design, develop, and optimize high-quality code for complex software applications and systems, maintaining high standards of performance, scalability, and maintainability. Drive best practices in code quality, documentation, and test coverage.

GenAI Product Development: Lead end-to-end development of generative AI solutions, from data collection and model training to deployment and optimization. Experiment with cutting-edge generative AI techniques to enhance product capabilities and performance.

Technical Leadership: Take ownership of architecture and technical decisions for AI/ML projects. Mentor junior engineers, review code for adherence to best practices, and ensure the team follows a high standard of technical excellence.

Project Ownership: Lead execution and delivery of features, managing project scope, timelines, and priorities in collaboration with product managers. Proactively identify and mitigate risks to ensure successful, on-time project completion.

Architectural Design: Contribute to the architectural design and planning of new features, ensuring solutions are scalable, reliable, and maintainable. Engage in technical reviews with peers and stakeholders, promoting a product suite mindset.

Code Review & Best Practices: Conduct rigorous code reviews to ensure adherence to industry best practices in coding standards, maintainability, and performance optimization. Provide feedback that supports team growth and technical improvement.

Testing & Quality Assurance: Design and implement robust test suites to ensure code quality and system reliability. Advocate for test automation and the use of CI/CD pipelines to streamline testing processes and maintain service health.

Service Health & Reliability: Monitor and maintain the health of deployed services, utilizing telemetry and performance indicators to proactively address potential issues. Perform root cause analysis for incidents and drive preventive measures for improved system reliability.

Educational : Bachelor’s degree in computer science, Engineering, or a related technical field; Master’s degree preferred.

Here’s what will give you an edge…

Technical Expertise: Advanced proficiency in Python, FastAPI, PyTest, Celery, and other Python frameworks. Deep knowledge of software design patterns, object-oriented programming, and concurrency.

Cloud & DevOps Proficiency: Extensive experience with cloud technologies (e.g., GCP, AWS, Azure), containerization (e.g., Docker, Kubernetes), and CI/CD practices. Strong understanding of version control systems (e.g., GitHub) and work tracking tools (e.g., JIRA).

AI/GenAI Knowledge: Familiarity with GenAI frameworks (e.g., LangChain, LangGraph), MLOps, and AI lifecycle management. Experience with model deployment and monitoring in cloud environments.

Preferred Skill sets/Tools:

AI & Machine Learning: Hands-on experience with advanced ML algorithms, including generative models, NLP, and transformers. Knowledge of industry-standard AI frameworks (e.g., TensorFlow, PyTorch) and experience with data preprocessing and model evaluation.

Data & Analytics Tools: Proficiency with relational and NoSQL databases (e.g., MongoDB, MSSQL, PostgreSQL) and analytics platforms (e.g., BigQuery, Snowflake, Tableau). Experience with messaging systems (e.g., Kafka) is a plus.

Testing & Quality: Experience with test automation tools (e.g., PyTest, xUnit) and CI/CD tooling such as Terraform and GitHub Actions. Strong emphasis on building resilient and testable software.

Advanced Cloud Knowledge: Proficiency with GCP technologies such as VertexAI, BigQuery, GKE, GCS, and DataFlow, with a focus on deploying AI models at scale.

#LI-UB1

Job Title: Senior Software Engineer
Locations: Bangalore
Reports to: Director, Software Engineering

A quick snapshot…
As a Senior Software Engineer, you’ll be a key member in a team responsible for the development of scalable, reliable, and innovative AI/GenAI solutions. You’ll contribute to high-priority projects, ensuring maintainable code and producing high-quality, production-ready applications. You’ll operate with a high degree of technical expertise, strategic thinking, effective collaboration across diverse teams, while helping to mentor and elevate others to meet a very high technical bar.   

Why it’s a big deal…  

You will be a key contributor to delivering Conga’s AI roadmap, ensuring solutions meet high quality standards and timelines. You’ll be a core member of the team that will implement complex, large-scale AI systems. You’ll also have the opportunity to work cross-functionally to resolve issues, mentor junior members, and help to foster a culture of continuous learning and technical excellence. All of this adds up to an exciting, challenging, and always interesting place to work, where complex problems are found and solved every day.

Here's what we’re looking for…

Related Experience. You have 7+ years of professional software development experience, including 3+ years in AI/ML or GenAI. You have a proven track record of developing and deploying scalable, production-grade AI solutions. 

Technical expertise. You have:  

• Experience in Python and frameworks such as TensorFlow, PyTorch, FastAPI, LangChain, LangGraph, and KubeFlow.  

• Knowledge of AI/ML algorithms, generative models, and LLMs. 

• Proficiency with cloud platforms (e.g., GCP, AWS, Azure) and modern DevOps practices. 

• Strong understanding of distributed systems, microservices architecture, and database systems (SQL/NoSQL). 

Education. A bachelor’s degree in engineering or equivalent.

Here’s what will give you an edge…

Effective communication and interpersonal skills. You’re comfortable initiating and engaging in collaborative discussions. You are skilled at reading and adapting to different communication styles. When you speak, you are clear and concise. Your strong listening skills help to build trust and foster connections with our clients. They also ensure you collect the right information so you can resolve issues most expediently.

Initiative. You do not wait around for things to happen or for someone to tell you what to do. You are not only proactive about completing your own work, but when you sense the need to introduce a project that will benefit the team or the organization -- even if it’s outside your scope of work -- you put a proposal together, talk to the team about it, and own it. And that also goes back to having an entrepreneurial spirit.

Passionate Problem Solver. Proactive, self-driven, and enjoys solving complex problems, not just in the primary area of responsibility but across boundaries. Identify and implement best practices for coding as well as development processes and perform as a trusted contributor requiring little to no supervision and demonstrating the ability to meet deadlines, exceed goals, and create benchmarks for success.

#Li-VA1

Job Title: Senior Software Engineer, AI/ML or GenAI
Locations: Bangalore / Ahmedabad / Pune

Reports to: Director, Software Engineering

A quick snapshot…

As a Senior Software Engineer, you’ll be a key member in a team responsible for the development of scalable, reliable, and innovative AI/GenAI solutions. You’ll contribute to high-priority projects, ensuring maintainable code and producing high-quality, production-ready applications. You’ll operate with a high degree of technical expertise, strategic thinking, effective collaboration across diverse teams, while helping to mentor and elevate others to meet a very high technical bar.   

Why it’s a big deal…  

You will be a key contributor to delivering Conga’s AI roadmap, ensuring solutions meet high quality standards and timelines. You’ll be a core member of the team that will implement complex, large-scale AI systems. You’ll also have the opportunity to work cross-functionally to resolve issues, mentor junior members, and help to foster a culture of continuous learning and technical excellence. All of this adds up to an exciting, challenging, and always interesting place to work, where complex problems are found and solved every day.

Here's what we’re looking for…

Related Experience. You have 7+ years of professional software development experience, including 3+ years in AI/ML or GenAI. You have a proven track record of developing and deploying scalable, production-grade AI solutions. 

Technical expertise. You have:  

• Experience in Python and frameworks such as TensorFlow, PyTorch, FastAPI, LangChain, LangGraph, and KubeFlow.  

• Knowledge of AI/ML algorithms, generative models, and LLMs. 

• Proficiency with cloud platforms (e.g., GCP, AWS, Azure) and modern DevOps practices. 

• Strong understanding of distributed systems, microservices architecture, and database systems (SQL/NoSQL). 

Education. A bachelor’s degree in engineering or equivalent.

Here’s what will give you an edge…

Effective communication and interpersonal skills. You’re comfortable initiating and engaging in collaborative discussions. You are skilled at reading and adapting to different communication styles. When you speak, you are clear and concise. Your strong listening skills help to build trust and foster connections with our clients. They also ensure you collect the right information so you can resolve issues most expediently.

Initiative. You do not wait around for things to happen or for someone to tell you what to do. You are not only proactive about completing your own work, but when you sense the need to introduce a project that will benefit the team or the organization -- even if it’s outside your scope of work -- you put a proposal together, talk to the team about it, and own it. And that also goes back to having an entrepreneurial spirit.

Passionate Problem Solver. Proactive, self-driven, and enjoys solving complex problems, not just in the primary area of responsibility but across boundaries. Identify and implement best practices for coding as well as development processes and perform as a trusted contributor requiring little to no supervision and demonstrating the ability to meet deadlines, exceed goals, and create benchmarks for success.

#Li-VA1

Solutions Architect

Role Overview 

As Solutions Architect you operate at the intersection of deep technical leadership and high-stakes client engagement. You will: 

• Own solution architecture and technical delivery for marquee customers, ensuring JazzX’s AGI platform unlocks measurable business value. 
• Guide multi-disciplinary squads (product, research, engineering, and customer success) from discovery through full production rollout. 
• Shape platform roadmap with field feedback, driving features that accelerate time-to-value and differentiate JazzX in the market. 

You are equally comfortable white-boarding LLM retrieval strategies, optimising prompt chains, troubleshooting Kubernetes deployments, or presenting to a C-suite audience. 

Key Responsibilities 

Qualifications 

Must-Have 

• 10–12 years of software engineering experience, 5+ in staff/principal or solution-architect roles. 
• Proven record delivering large-scale, low-latency distributed systems (Python, Java, Go, or C++). 
• Hands-on with LLM / AI frameworks (LangChain, LlamaIndex, PyTorch, TensorFlow) and productionising ML pipelines. 
• Expertise integrating REST/GraphQL APIs, streaming platforms (Kafka), SQL & NoSQL stores, and vector DBs. 
• Outstanding client-facing communication, executive-level presentation, and stakeholder-management skills. 

Nice-to-Have 

• Masters or PhD in AI/Data Science/Computer Science 
• Experience shipping RAG or agent-based AI systems in regulated industries. 
• Nice to have certifications in cloud architecture, data engineering, or security (e.g., AWS SA-Pro, CKA). 
• Background in professional-services, forward-deployed engineering, or AI platform product management. 

Attributes 

• Empathy & Ownership: You listen carefully to user needs and take full ownership of delivering great experiences. 
• Startup Mentality: You move fast, learn quickly, and are comfortable wearing many hats. 
• Detail-Oriented: You care about the little things  
• Mission-Driven: You want to solve important, high-impact problems that matter to real people. 
• Team-Oriented: Low ego, collaborative, and excited to build alongside highly capable engineers, designers, and domain experts.  

Travel:

This position requires the ability to travel to client sites as needed for on-site deployments and collaboration. Travel is estimated at approximately 30–40% of the time (varying by project), and flexibility is expected to accommodate key client engagement activities. 

Summary:
Tekion seeks an exceptionally talented and seasoned Senior Staff Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is a seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale. 

Key Responsibilities:

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges.
• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions.
• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives.
• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services.
• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation.
• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth.
• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership.
• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.
• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.
• Deep Learning:
  • Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch.
  • Extensive experience implementing advanced neural network architectures.
  • Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies.
• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications.
• Technical Proficiencies:
  • Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous.
  • Proficiency with cloud computing platforms (GCP, Azure, AWS).
  • Expertise in experimental design (A/B testing, causal inference).
  • Proficient in version control systems (Git).

Required Qualifications

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field.
• A minimum of 10+ years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value.
• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach.
• Proven experience in programming, data manipulation, and building models in a business context.
• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences.

Ideal Candidate Profile

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving.
• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable.
• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred.
• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences.
• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams.
• A commitment to continuous personal and team development within a collaborative, high-performance culture.

Address:

Bangalore Office  

Tekion India Private Limited 

6th, 7th & 8th Floor, Helium, Bagmane Solarium City, Doddanekkundi Village, KR Puram Hobli, East Taluk, Bangalore - 560037 

Chennai Office

Embassy Splendid Zone, Block 2, 9th floor, Velan Nagar, Arulmurugan Nagar, Old Pallavaram, Chennai, Tamil Nadu 600043

At Reltio®, we believe data should fuel business success. Reltio’s AI-powered data unification and management capabilities—encompassing entity resolution, multi-domain master data management (MDM), and data products—transform siloed data from disparate sources into unified, trusted, and interoperable data. Reltio Data Cloud™ delivers interoperable data where and when it's needed, empowering data and analytics leaders with unparalleled business responsiveness. Leading enterprise brands—across multiple industries around the globe—rely on our award-winning data unification and cloud-native MDM capabilities to improve efficiency, manage risk and drive growth.

At Reltio, our values guide everything we do. With an unyielding commitment to prioritizing our “Customer First”, we strive to ensure their success. We embrace our differences and are “Better Together” as One Reltio. We are always looking to “Simplify and Share” our knowledge when we collaborate to remove obstacles for each other. We hold ourselves accountable for our actions and outcomes and strive for excellence. We “Own It”. Every day, we innovate and evolve, so that today is “Always Better Than Yesterday”. If you share and embody these values, we invite you to join our team at Reltio and contribute to our mission of excellence.

Reltio has earned numerous awards and top rankings for our technology, our culture and our people. Reltio was founded on a distributed workforce and offers flexible work arrangements to help our people manage their personal and professional lives. If you’re ready to work on unrivaled technology where your desire to be part of a collaborative team is met with a laser-focused mission to enable digital transformation with connected data, let’s talk!

About the Role

We are seeking a Senior Backend Engineer with a strong foundation in Java, solid Python capabilities, and hands-on experience building Agentic AI applications that integrate with Large Language Models (LLMs). This role blends traditional backend engineering expertise with next-generation AI agentic systems development, requiring not just system design skills, but also creativity in prompt engineering, AI product development and working with data-rich environments.

You will contribute to the design and implementation of intelligent backend services that coordinate LLMs, agents, and data workflows to support enterprise-grade agentic systems.

Key Responsibilities

• Develop robust, scalable Java/Python based backend systems that enable the orchestration of Agentic AI workflows.
• Develop state-of-the-art AI/ML models for solving real-world data management challenges such as entity resolution, classification, similarity matching, and anomaly detection.
• Integrate LLMs (e.g., OpenAI, Mistral, Claude, LLaMA) into backend pipelines to power autonomous or semi-autonomous decision-making.
• Develop scalable ML workflows using Spark, MLlib, PyTorch, TensorFlow, or MLFlow, with seamless integration into production systems.
• Translate business needs into technical design and collaborate with data scientists, product managers, and platform engineers to operationalize models.
• Work closely with various stakeholders to design agent architectures and LLM toolchains (using frameworks like LangGraph, Crew AI, AutoGen, etc.).
• Implement services and APIs that manage prompt chaining, tool invocation, and contextual memory for agents.
• Collaborate with data engineers to ensure clean, efficient, and real-time access to structured and semi-structured data.
• Apply prompt engineering best practices to improve agent behavior, task accuracy, and adaptability.
• Monitor and optimize service performance, reliability, and scalability in production environments.
• Continuously monitor and improve model performance using feedback loops, A/B testing, drift detection, and retraining strategies.
• Contribute to code reviews, mentoring, and best practices for hybrid backend/AI development.

Required Qualifications

• 5+ years of overall experience in backend engineering, with strong proficiency in Java for scalable, distributed systems.
• 2+ years of experience in developing and deploying machine learning models and AI products in production environments.
• Experience in Agentic AI systems development using tools such as LangGraph, Crew AI, AutoGen, or similar.
• Proficiency in Python (NumPy, scikit-learn, pandas, PyTorch/TensorFlow) and experience with large-scale data processing tools (Spark, Kafka, Airflow).
• Experience integrating or orchestrating LLMs within business workflows or intelligent assistants.
• Solid understanding of prompt engineering techniques (e.g., system messages, chaining, tool invocation prompts).
• Familiarity with data engineering concepts, including pipelines, data models, and ETL workflows.
• Strong grasp of object-oriented design, data structures, and algorithms.

Preferred Qualifications

• Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, RAG pipelines).
• Background in building microservices or backend systems for data-rich AI/ML platforms.
• Experience with vector databases, embedding services, or semantic search integration.
• Knowledge of cloud platforms (AWS, GCP, or Azure), containerization (Docker), and DevOps best practices.
• Experience in developing LLM-powered agents, copilots, or task bots.

What We Offer

• Opportunity to work on cutting-edge backend and AI systems, including agentic automation and LLM innovation.
• A flexible hybrid work model in our Bangalore office.
• Competitive compensation with performance-based rewards.
• A culture that promotes engineering excellence, experimentation, and growth.Access to learning platforms, technical workshops, and industry-leading AI research.

Why Join Reltio?*

• Health & Wellness:
  • Comprehensive Group medical insurance including your parents with additional top-up options.
  • Accidental Insurance
  • Life insurance
  • Free online unlimited doctor consultations
  • An Employee Assistance Program (EAP)
• Work-Life Balance:
  • 36 annual leaves, which includes Sick leaves – 18, Earned Leaves - 18
  • 26 weeks of maternity leave, 15 days of paternity leave
  • Very unique to Reltio - 01 week of additional off as recharge week every year globally
• Support for home office setup:
  • Home office setup allowance.
• Stay Connected, Work Flexibly: Mobile & Internet Reimbursement
• No need to pack a lunch—we’ve got you covered with a free meal.And many more…..

What You’ll Do:

• Architect the Future of AI Infrastructure: You will design, build, and own the end-to-end platform that supports the entire lifecycle of our ML models—from massive-scale distributed training to ultra-low-latency, highly-available inference.
• Optimize and Serve Cutting-Edge Models: You'll implement and scale sophisticated inference stacks for LLMs using frameworks like vLLM, TensorRT-LLM, or SGLang. You’ll solve complex challenges in throughput, latency, token streaming, and automated scaling to deliver a seamless user experience. 
• Empower AI Innovation: You will act as a strategic partner to our AI Research and Data Science teams. You’ll create a seamless developer experience that accelerates their ability to experiment, fine-tune, and deploy groundbreaking models with velocity and confidence.
• Automate Everything: You'll develop robust CI/CD/CT (Continuous Training) pipelines using tools like Argo Workflows, ArgoCD, and GitHub Actions to automate model validation, deployment, and lifecycle management, ensuring our systems are both agile and rock-solid.

What are we looking for

• Experience: 5+ years in infrastructure or software engineering, with at least 2+ years laser-focused on MLOps or ML infrastructure for large-scale distributed systems.
• Education: A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Kubernetes & Cloud Native Expertise: Deep, hands-on expertise with Kubernetes in production. You are fluent in the cloud-native ecosystem, including Helm, ArgoCD, and Argo Workflows.
• GPU & Cloud Mastery: Optimize the platform’s performance and scalability, considering factors such as GPU resource utilization, data ingestion, model training, and deployment.
• Modern LLM Serving Experience: Hands-on experience with modern LLM inference serving frameworks (e.g., vLLM, SGLang, Triton Inference Server, Ray Serve). You understand the unique challenges of serving generative models.
• Strong Coder: Strong programming proficiency in Python or Go, with experience using ML frameworks like PyTorch, Jax, TensorFlow.
• Observability Mindset: A passion for building observable and resilient systems using modern monitoring tools (e.g., Prometheus, Grafana, OpenTelemetry).

We would love to see:

• Deep performance optimization skills, including writing custom inference kernels in CUDA or Triton to accelerate model performance beyond what off-the-shelf frameworks provide.
• Experience with model optimization techniques like quantization, distillation, and speculative decoding.
• Exposure to training and serving multi-modal models (e.g., text-to-image, vision-language).
• Knowledge of AI safety and evaluation frameworks for monitoring model performance for things like bias, toxicity, and hallucinations.

As part of our hiring process, shortlisted candidates will undergo a Background Verification (BGV). By applying, you consent to sharing personal information required for this process. Any offer made will be subject to successful completion of the BGV.

Staff Software Development Engineer  

As a Staff Software Development Engineer at JazzX AI, you'll define the technical vision for our enterprise AI platform, mentor senior engineers, and oversee the design, development, and deployment of critical AI, software and infra services and components.  You'll be responsible for ensuring the platform's scalability, reliability, security and observability. 

What You Will Do 

Design and Architect Scalable Solutions: 

• Lead the design and development of high-performance, scalable services and components for our enterprise AI platform using a cloud-first, cloud-agnostic approach with world class AI capabilities, scalability, security, privacy and operability.
• Champion a cloud-first, cloud-agnostic approach to ensure platform versatility, scalability, and future-proof architecture.
• Champion innovative AI and software design patterns and best practices to ensure code maintainability, reusability, and testability.
• Contribute to the platform's technical direction by staying up to date on emerging AI technologies, cloud design patterns and user experience methodologies. 

Collaborative Innovation: 

• Foster a collaborative environment by working closely with cross-functional teams (AI/research engineers, software engineers, architects, product managers, and designers) to translate complex platform and product requirements into elegant, scalable, and modular AI, software and infrastructure components.
• Foster a collaborative and inclusive environment by effectively communicating technical concepts and design decisions across diverse audiences.
• Provide mentorship and guidance to junior engineers, fostering their growth and development. 

Driving Operational Excellence: 

• Oversee the seamless integration of components into the platform, ensuring efficient operation, reliability, and a dependable user experience for both customers and partners.
• Champion DevOps and AIOps best practices for AI services, maintainability, performance optimization, and observability. 

Requirements 

• Minimum 10+ years of experience designing, building, and operating cloud-native applications at scale. Expertise in container orchestration frameworks like Kubernetes and containerization technologies like Docker. Expertise in AI/ML/data capabilities, platforms, frameworks and tools.
• Proven track record deploying and managing cloud environments using Infrastructure as Code (IaC) tools like Terraform, Ansible, or CloudFormation.
• Experience in designing and implementing highly available, scalable, and fault-tolerant systems. 

Development Lifecycle: 

• End-to-end ownership of the entire software development lifecycle (SDLC) for complex enterprise platforms and products. This includes experience in design, prototyping, development (including unit, integration, and end-to-end testing), deployment, and ongoing operations. 

Technical Skills: 

• Mastery of essential software development principles with a focus on clean code, design patterns, and best practices.
• In-depth expertise in at least two of the following programming languages: Python, Java, JavaScript, Golang, or C++.
• Solid understanding of distributed systems concepts and experience with distributed tracing tools. 

AI/ML Experience: 

• Demonstrated experience in building, deploying, and managing Machine Learning (ML) and Artificial Intelligence (AI) platforms and services. Familiarity with MLOps practices, frameworks (TensorFlow, PyTorch), and tools is a plus 
• Familiarity with modern LLMs and AI agent frameworks like LangChain, LlamaIndex , AutoGen etc. is a strong plus 

Opportunity

Get Well is seeking a talented and innovative Senior AI Engineer to lead the development and deployment of advanced AI solutions within a mission-driven healthcare environment. This role is ideal for a seasoned professional with hands-on experience in machine learning (ML), large language models (LLMs), and generative AI (GenAI), with a strong focus on productionizing scalable and reliable models.

As a Senior AI Engineer, you will drive end-to-end AI development—from ideation and training data pipelines to model deployment and continuous improvement—while addressing unique healthcare data challenges. You’ll collaborate across functions including product, engineering, data science, and clinical experts to deliver high-impact AI applications, including voice assistants and multimodal systems tailored to real-world care settings.

Responsibilities

AI Model Development & Customization

• Fine-tune SLMs / LLMs and develop ML models for healthcare-specific use cases.
• Build multimodal AI systems that integrate text, structured medical data, and images.
• Optimize models for accuracy, latency, and resource efficiency in production settings.
• Evaluate and integrate speech-to-text (STT) and text-to-speech (TTS) models into conversational interfaces.
• Customize foundation models using domain adaptation and prompt engineering techniques (e.g., PEFT, LoRA).
• Develop AI algorithms from healthcare data and guide them through a full production lifecycle into deployed solutions.

Productionization & Deployment

• Lead model deployment end-to-end with cloud-native tools and infrastructure (Azure ML).
• Implement model performance monitoring, drift detection, and auto-retraining pipelines.
• Ensure AI solutions are scalable, reliable, and aligned with security and compliance requirements in healthcare environments.

Technical Leadership

• Provide technical guidance to junior AI engineers.
• Conduct design reviews and drive the adoption of best practices in model reproducibility, validation, and explainability.
• Lead the evaluation and integration of cutting-edge ML research and open-source frameworks.
• Contribute to architectural decisions aligned with clinical, business, and regulatory goals.

Data Engineering & Management

• Process and curate large-scale, structured and unstructured healthcare datasets.
• Design synthetic data generation strategies where needed to augment training.
• Handle noisy, imbalanced, or incomplete data through robust preprocessing and enrichment.
• Ensure HIPAA and GDPR compliance in data handling, encryption, and access management.
• Leverage EHR and clinical data from the provider side to engineer healthcare data pipelines and training corpora.

Bias Mitigation, Evaluation & Governance

• Lead the creation of robust evaluation strategies combining domain-specific KPIs (e.g., AUC, accuracy) with clinical relevance.
• Proactively identify, measure, and mitigate algorithmic bias, especially in patient-facing models.
• Conduct adversarial stress testing and ensure models meet safety, fairness, and ethical standards.
• Define model KPIs aligned with measurable impacts on clinical outcomes, patient safety, and workflow efficiency.

Cross-Functional Collaboration

• Translate stakeholder and product needs into robust, scalable AI solutions.
• Collaborate with clinical experts to vet model behavior and expected impact in patient and caregiver workflows.
• Participate in Agile processes including sprint planning, demos, and technical retrospectives.
• Manage documentation of model architectures, version changes, testing strategies, and key design decisions.

Learning, Innovation & Thought Leadership

• Stay up-to-date on the latest LLM advancements, STT/TTS developments, voice interaction technologies, and GenAI frameworks.
• Evaluate new models and methods (e.g., RAG, LangChain, Whisper, Tacotron) for their potential impact and feasibility.
• Advocate for responsible AI and lead initiatives in privacy-preserving and explainable ML.
• Contribute to a culture of continuous learning, internal knowledge sharing, and open technical dialogue.

Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
• Minimum 6+ years of industry experience in AI/ML including at least 2 years with GenAI, NLP/NLU, and LLMs.
• Must have software development experience at a healthcare company, particularly in the provider space (e.g., hospitals, clinical systems) with exposure to EHRs or related healthcare data domains.
• Proven experience developing AI algorithms using healthcare datasets and integrating them into production products or services.
• Experience building voice-enabled products or assistants is highly advantageous.

Technical Skills

• Expert in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with training/fine-tuning LLMs and modern NLP/NLU techniques.
• Hands-on expertise with STT (e.g., Whisper) and TTS (e.g. Elevenlabs) models.
• Proficient in MLOps, including model serving, experiment tracking, container orchestration, and CI/CD pipelines.
• Strong understanding of production-grade solutions using Azure.
• Familiarity with version control (e.g., Git), collaboration tools, and responsible AI practices.
• Familiar with vector databases, retrieval-augmented generation (RAG), and API deployment.
• Comfortable with healthcare regulatory frameworks (e.g., HIPAA, GDPR) and ethical data use policies.

Professional Attributes

• Curious and self-motivated, with a passion for impactful healthcare innovation.
• Effective communicator who can tailor technical concepts to non-technical stakeholders.
• Exceptional problem-solving and quantitative reasoning abilities.
• Team-oriented, growth-minded, and proactive in driving projects forward.
• Highly organized and focused on quality, reproducibility, and compliance.
• Passionate about applying AI to improve outcomes in real-world healthcare systems.

About GW RhythmX

GW RhythmX is revolutionizing healthcare through connected, AI-native intelligence that unites clinical insight, patient engagement, and system-wide care orchestration. The company combines market-leading AI precision care technology with extensive trusted patient engagement leadership to help health systems deliver the right care, at the right time, through the right clinician and channel. Its solutions are deployed across more than 150 health systems, touching more than 85M patients including 8M U.S. military veterans. The company's award-winning solutions were recognized again in 2024 by KLAS Research, Fierce Healthcare, and AVIA Marketplace. A SymphonyAI Group company, GW RhythmX leverages various firm assets, including $1B+ in R&D investment, longitudinal data related to 300 million patients, 4.4 billion total annual claims, and 1.8 million healthcare professionals at more than 3,000 facilities globally.

GW RhythmX  is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.

Opportunity

Get Well is seeking a highly experienced and innovative Staff AI Engineer to lead the architecture, development, and optimization of cutting-edge AI solutions across the organization’s healthcare platform. This role requires deep technical expertise in large language models (LLMs), multimodal AI systems, agentic frameworks, and voice technologies—including STT (speech-to-text) and TTS (text-to-speech).

As a Staff-level technical leader, you will guide the AI engineering lifecycle from conceptualization to deployment at scale while serving as a key cross-functional partner to product, engineering, and clinical teams. This is a high-impact, hands-on role for a forward-thinking AI expert with a strong understanding of emerging agentic systems and best practices in safety, observability, and machine learning operations in regulated environments.

Responsibilities

Healthcare-Focused AI System Design & Development

• Architect and develop production-ready AI models trained on real-world clinical datasets sourced from hospital systems, EHRs, and patient engagement platforms.
• Partner with clinical informatics and product teams to derive insights from structured and unstructured health data, including FHIR, HL7, CCDA, and EHR notes.
• Design and train speech-to-text (STT) and text-to-speech (TTS) models to power voice-enabled AI applications and virtual assistants in a healthcare setting.
• Integrate and optimize agentic systems using frameworks such as LangChain, LangGraph, or CrewAI for autonomous decision-making and workflow automation.
• Drive the end-to-end development lifecycle—from data prep and model training to evaluation, deployment, and monitoring—ensuring responsiveness and efficiency in high-impact healthcare settings.
• Evaluate and incorporate emerging AI technologies and architectural tools to improve intelligence, personalization, and user experience.

Infrastructure Optimization & MLOps

• Lead the model lifecycle from ingestion and preprocessing of healthcare datasets (e.g., EHR records, patient surveys, clinical measurements) to training, evaluation, and deployment into hospital IT ecosystems.
• Lead the design and optimization of cloud-based AI infrastructure, focusing on scalability, performance, observability, and cost-efficiency (Azure preferred).
• Establish and maintain scalable CI/CD pipelines, GPU-optimized runtimes, and real-time or batch inference systems in Azure healthcare-compliant environments.
• Ensure reliability, production-grade observability, and rollback safeguards using tools like Langfuse, Prometheus, Grafana, and other internal tools.

Monitoring, Observability & Reliability

• Set up and manage observability tools and frameworks such as Langfuse, Prometheus, Grafana, or equivalent to monitor operational health of AI models and agentic workflows.
• Establish proactive monitoring for model performance, agent behavior, anomaly detection, and feedback loop management.
• Rapidly diagnose and address system bottlenecks, drift, or failure points in production environments.

Healthcare Compliance & Responsible AI

• Ensure all AI solutions adhere to HIPAA, GDPR, and internal privacy and data security standards.
• Design and enforce ethical AI principles, focusing on bias mitigation, explainability, reproducibility, and accountability.
• Oversee secure handling and governance of sensitive data, including ePHI and PHI, in compliance with Federal, State, and local regulations.

Cross-Functional Collaboration & Technical Leadership

• Act as a principal technical liaison between AI engineering, product, design, and clinical stakeholders.
• Translate complex technical architectures into product-aligned features and user-centric outcomes.
• Collaborate closely with clinical experts to ensure AI solutions address high-impact, evidence-based healthcare needs.
• Mentor and elevate junior engineers through architecture reviews, hands-on pairing, and code quality leadership.
• Drive a culture of innovation, excellence, and learning across AI engineering and data science teams.

Qualifications

Education & Experience

• Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field.
• 8–10+ years of hands-on experience in AI/ML development, with 3+ years in technical leadership or Staff/Principal-level roles.
• Proven track record of delivering production-grade AI systems in healthcare industries.

Technical Expertise

• Deep expertise in:
  • LLMs (e.g., OpenAI, LLaMA, Claude) and transformer-based NLP models
  • Multimodal learning architectures integrating text, image, and structured healthcare data
  • Fine-tune SLMs / LLMs and develop ML models for healthcare-specific use cases.
  • Agentic AI systems using frameworks like CrewAI, LangChain, and LangGraph
  • STT/TTS models (e.g., Whisper, Tacotron, FastSpeech, DeepSpeech)
• Advanced programming in Python (required); familiarity with C++ is a plus.
• Proficient in AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and model serving stacks.
• Hands-on experience with MLOps frameworks, infrastructure-as-code, container orchestration, and model registries.
• Familiarity with healthcare data standards (FHIR, HL7, SNOMED, ICD-10) and clinical integration best practices
• Awareness of cutting-edge trends in Agentic Systems, Multimodal Context Processing (MCP), A2A (Agent-to-Agent) protocols, and healthcare-centric AI safety practices.

Professional Attributes

• Strong analytical and problem-solving abilities with a bias for action.
• Excellent communicator—able to translate complex AI systems to diverse stakeholders.
• Proven ability to work in fast-paced, cross-functional, and agile product environments.
• Committed to high standards of privacy, compliance, and ethical AI.
• Demonstrated experience mentoring engineers and influencing platform and product direction through thought leadership.
• Adaptability to rapid technological shifts and emerging AI frameworks.

About GW RhythmX

GW RhythmX is revolutionizing healthcare through connected, AI-native intelligence that unites clinical insight, patient engagement, and system-wide care orchestration. The company combines market-leading AI precision care technology with extensive trusted patient engagement leadership to help health systems deliver the right care, at the right time, through the right clinician and channel. Its solutions are deployed across more than 150 health systems, touching more than 85M patients including 8M U.S. military veterans. The company's award-winning solutions were recognized again in 2024 by KLAS Research, Fierce Healthcare, and AVIA Marketplace. A SymphonyAI Group company, GW RhythmX leverages various firm assets, including $1B+ in R&D investment, longitudinal data related to 300 million patients, 4.4 billion total annual claims, and 1.8 million healthcare professionals at more than 3,000 facilities globally.

GW RhythmX  is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore. For more details visit: www.netradyne.com

Job Overview:

As a Staff Data Engineer – ML at Netradyne, an award-winning industry leader in fleet safety and video telematics solutions, you will take senior technical ownership of the machine learning and data platforms that underpin our AI-powered fleet analytics. This role is responsible for designing, building, and scaling production-grade ML pipelines, generative AI capabilities, and real-time data streaming systems across cloud and edge environments, delivering actionable insights that make fleet operations safer and more efficient. You will collaborate closely with machine learning engineers, data scientists, and product teams to integrate advanced AI technologies into Netradyne's platform, including on-device edge intelligence and natural-language-driven capabilities. Your work will directly shape the data infrastructure behind Netradyne's Physical AI vision, where AI continuously interprets multimodal vehicle data to understand drivers, vehicles, and road environments as an integrated system.

Key Responsibilities:

• You will be embedded within a team of machine learning engineers and data scientists, responsible for building and productising generative AI, deep learning, and data engineering solutions. You will:
• Design, develop and deploy production-ready, scalable solutions that utilise generative AI, traditional ML models, data science workflows, and ETL/ELT pipelines on AWS cloud infrastructure (and hybrid edge-cloud environments).
• Build and manage real-time and batch data streaming pipelines to handle high-volume, high-velocity data from fleet devices, leveraging technologies such as Apache Kafka and Amazon Kinesis for low-latency processing and near real-time insights.
• Collaborate with cross-functional teams (Product, Data Science, ML Engineering, Operations) to integrate AI-driven solutions into business operations and customer-facing products.
• Design and implement generative AI solutions using large language models (LLMs), including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) patterns, to enable intelligent analytics, natural-language interfaces, and context-aware insights across fleet and safety data.
• Build and scale agentic AI systems by developing autonomous and semi-autonomous agents that orchestrate data retrieval, reasoning, and actions across ML pipelines and services, ensuring these agentic workflows are production-grade, observable, and aligned with safety, reliability, and governance requirements.
• Implement MLOps best practices including CI/CD pipelines for model training, automated testing, model versioning, continuous monitoring, and modern workflow orchestration (e.g. Apache Airflow, MLFlow) to achieve reliable, reproducible model deployments at scale.
• Champion data quality and governance by implementing data contracts, rigorous validation, and monitoring to ensure that analytical pipelines meet defined SLAs and that downstream models and business users can trust the data.
• Conduct research and stay current with the latest advancements in generative AI, vector search techniques, LLM frameworks, orchestration platforms, and related technologies.

Mandatory Skills:

• Experience with data visualisation and BI tools (e.g. Tableau, Grafana, Plotly Dash).
• Exposure to real-time data streaming and messaging systems (e.g. Apache Kafka, Amazon Kinesis, Amazon SQS).
• Knowledge of containerisation and orchestration (e.g. Docker, Kubernetes/EKS)
• Strong understanding of generative AI system design, including prompt engineering, RAG architectures, vector search, and evaluation techniques for LLM-based applications in production environments.
• Experience with agentic AI patterns, such as designing, orchestrating, and monitoring autonomous or semi-autonomous agents that coordinate tools, data sources, and ML services with appropriate safety, observability, and governance controls.
• Experience with modern workflow orchestration platforms such as Apache Airflow, Prefect, or Dagster for scheduling and managing complex ETL/ML pipelines. These tools are now standard for data pipeline engineering.
• Proficiency with at least one Python web framework (e.g. FastAPI, Django, Flask) for developing data or model-serving APIs.
• Experience with rapid prototyping tools (e.g. Streamlit, Gradio, Dash) for demonstrating ML solutions to stakeholders.
• Familiarity with modern ML frameworks and libraries such as PyTorch, TensorFlow, and Hugging Face Transformers
• Background in large-scale data processing and lakehouse or data warehousing technologies (e.g. Snowflake, Amazon Redshift, Apache Spark/EMR, Delta Lake).

Qualifications and Education Requirements:

• B.Tech, M.Tech or PhD in Computer Science, Data Science, Electrical Engineering, Statistics, Mathematics, Operations Research, or a related field (or equivalent professional experience).
• 5–8 years of industry experience in data engineering, machine learning engineering, or related roles, with a track record of building and scaling data/ML solutions in production.
• Strong programming skills in Python and SQL, with solid fundamentals in algorithms, data structures, and object-oriented programming.
• Experience building end-to-end solutions on AWS cloud infrastructure including developing data pipelines (ETL/ELT), deploying ML models at scale, and utilising cloud services (e.g. S3, Lambda, EKS).
• In-depth understanding of data storage systems: proficient with relational databases (SQL), NoSQL datastores, and modern vector databases, including schema design and query optimisation for large-scale datasets.
• Hands-on experience with generative AI tools and workflows, and practical work with large language models (LLMs), including building LLM-powered applications and utilising RAG techniques to incorporate external knowledge.
• Working knowledge of MLOps principles and tools — e.g. model lifecycle management, CI/CD pipelines for ML, automated data/feature pipelines, and monitoring/alerting for deployed models.
• Strong analytical and problem-solving skills with keen attention to detail.
• Solid foundation in statistics, probability, and estimation theory.
  
  

Equal Opportunity Employer Statement:
Netradyne is an equal opportunity employer and are committed to creating an inclusive environment for all employees.

Job Title: Senior AGI Engineer – GTM Solution Delivery 

Role Overview 

As a Senior AGI Engineer you are the hands-on technical force that turns JazzX’s AGI platform into working, measurable solutions for customers. You will: 

• Build and integrate LLM-driven features, vector search pipelines, and tool-calling agents into client environments. 

• Collaborate with solution architects, product, and customer-success teams from discovery through production rollout. 

• Contribute field learnings back to the core platform, accelerating time-to-value across all deployments. 

You are as comfortable writing production-quality Python as you are debugging Helm charts, and you enjoy explaining your design decisions to both peers and client engineers. 

Key Responsibilities 

Qualifications 

Must-Have 

• 5+ years of professional software engineering experience; 2+ years working with ML or data-intensive systems. 

• Proficiency in Python (or Java/Go) with strong software-engineering fundamentals (testing, code reviews, CI/CD). 

• Hands-on experience deploying containerised services on AWS, GCP, or Azure using Kubernetes & Helm. 

• Practical knowledge of LLM / Gen-AI frameworks (LangChain, LlamaIndex, PyTorch, or TensorFlow) and vector databases. 

• Familiarity integrating REST/GraphQL APIs, streaming platforms (Kafka), and SQL/NoSQL stores. 

• Clear written and verbal communication skills; ability to collaborate with distributed teams. 

• Willingness to travel 10–20 % for key customer engagements. 

Nice-to-Have 

• Experience delivering RAG or agent-based AI solutions in regulated domains (finance, healthcare, telecom). 

• Cloud or Kubernetes certifications (AWS SA-Assoc/Pro, CKA, CKAD). 

• Exposure to MLOps stacks (Kubeflow, MLflow, Vertex AI) or data-engineering tooling (Airflow, dbt). 

Attributes 

• Empathy & Ownership: You listen carefully to user needs and take full ownership of delivering great experiences. 

• Startup Mentality: You move fast, learn quickly, and are comfortable wearing many hats. 

• Detail-Oriented: You care about the little things  

• Mission-Driven: You want to solve important, high-impact problems that matter to real people. 

• Team-Oriented: Low ego, collaborative, and excited to build alongside highly capable engineers, designers, and domain experts. 

Travel 

This position requires the ability to travel to client sites as needed for on-site deployments and collaboration. Travel is estimated at approximately 20–30% of the time (varying by project), and flexibility is expected to accommodate key client engagement activities. 

Opportunity:

Get Well is seeking a highly skilled and visionary Principal Engineer - AI Engineering to drive the development and implementation of advanced AI solutions. This role focuses on the technical execution of AI projects, leveraging expertise in Large Language Models (LLMs), multimodal AI systems, and agentic systems to create innovative healthcare solutions. The Principal/Staff Engineer will work closely with AI engineering teams, product teams, and cross-functional stakeholders to deliver scalable, reliable, and impactful AI technologies. This role serves as a key interface between AI engineering teams and product teams, translating technological capabilities into tangible healthcare solutions.

This position reports directly to the VP, Product Development, working collaboratively across multiple teams to advance the organization's AI capabilities and strategic objectives. 

Responsibilities:

 AI Solution Development

• Design, develop, and implement AI solutions leveraging unimodal and multimodal Large Language Models (LLMs) and agentic systems.
• Build and integrate agentic systems using frameworks like CrewAI, LangChain, and LangGraph to enable autonomous decision-making and workflow automation.
• Incorporate emerging AI technologies into existing product ecosystems to enhance functionality and user experience.
• Execute model training, fine-tuning, and optimization of inference engines for high-performance AI systems.
• Ensure technical excellence in AI solution design and implementation.
• Evaluate and integrate emerging AI technologies into existing product ecosystems.

Cross-Functional Collaboration

• Serve as primary liaison between AI engineering teams and product teams
• Translate technical capabilities into product requirements and user-centric solutions
• Help facilitate communication and alignment across engineering, product, and clinical teams
• Drive collaborative innovation processes and knowledge sharing

Infrastructure and Optimization

• Optimize cloud-based AI infrastructure for scalability, performance, and reliability.
• Create metrics and frameworks to evaluate the effectiveness and performance of AI solutions, including autonomous agent interactions
• Implement best practices for efficient AI system deployment and maintenance, including agentic workflows.

Performance and Quality Assurance

• Establish and maintain rigorous testing and validation protocols for AI models
• Create metrics and frameworks to evaluate the effectiveness and performance of AI solutions, including autonomous agent interactions.
• Drive continuous improvement in AI model accuracy, efficiency, and robustness.

Monitoring and Observability 

• Implement and manage monitoring solutions for AI systems to ensure operational health and performance.
• Utilize tools such as Langfuse, Prometheus, Grafana, or similar platforms to track model performance, latency, and resource utilization.
• Analyze observability data to identify and resolve issues in AI model behavior, agentic system performance, and system reliability.
• Establish proactive monitoring strategies to detect anomalies and ensure reliability of AI deployments.
• Create metrics and frameworks to evaluate the effectiveness and performance of AI solutions, including autonomous agent interactions.

Compliance and Ethical AI

• Ensure AI solutions adhere to healthcare regulatory standards and data privacy requirements (e.g., HIPAA, GDPR)
• Develop and enforce ethical AI guidelines
• Mitigate potential biases in AI model development
• Maintain high standards of responsible AI innovation

Requirements:

• Master's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field
• Minimum 5+ years of technical leadership experience in AI/ML technologies
• Proven track record of delivering complex AI solutions in healthcare or technologically advanced domains
• Deep technical proficiency in:
  • Large Language Model (LLM) architectures
  • Multimodal AI system design
  • Agentic system development using frameworks like CrewAI, LangChain, and LangGraph.
  • Machine learning model training and fine-tuning
  • Cloud computing infrastructure (AWS preferred)
  • AI/ML frameworks and development tools (e.g., TensorFlow, PyTorch, Hugging Face)
• Experience with AI monitoring and observability tools such as Langfuse, Prometheus, Grafana, or equivalent platforms.
• Strong programming skills in Python, C++, or other relevant languages.
• Experience in agile and adaptive project management methodologies
• Exceptional problem-solving and analytical skills, with a focus on delivering scalable solutions.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Collaborative mindset with experience working in cross-functional teams.
• Ability to mentor junior engineers and contribute to team development.
• Knowledge of healthcare technology landscapes and regulatory environments (e.g., HIPAA, GDPR) is a plus.
• Awareness of emerging AI technologies, including agentic systems, MCP, A2A protocol and their potential applications.
• Adaptability to work in fast-evolving technological environments.
• Commitment to adhering to organizational information security policies and protecting sensitive information, including ePHI and PHI, in accordance with Federal, State, and local regulations.

About GW RhythmX

GW RhythmX is revolutionizing healthcare through connected, AI-native intelligence that unites clinical insight, patient engagement, and system-wide care orchestration. The company combines market-leading AI precision care technology with extensive trusted patient engagement leadership to help health systems deliver the right care, at the right time, through the right clinician and channel. Its solutions are deployed across more than 150 health systems, touching more than 85M patients including 8M U.S. military veterans. The company's award-winning solutions were recognized again in 2024 by KLAS Research, Fierce Healthcare, and AVIA Marketplace. A SymphonyAI Group company, GW RhythmX leverages various firm assets, including $1B+ in R&D investment, longitudinal data related to 300 million patients, 4.4 billion total annual claims, and 1.8 million healthcare professionals at more than 3,000 facilities globally.

GW RhythmX  is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.

Opportunity:

Get Well is seeking a highly skilled and proficient Staff Engineer - AI Engineering to drive the development and implementation of advanced AI solutions. This role focuses on the technical execution of AI projects, leveraging expertise in Large Language Models (LLMs), multimodal AI systems, and agentic systems to create innovative healthcare solutions. You will work closely with AI engineering teams, product teams, and cross-functional stakeholders to translate technological capabilities into impactful healthcare solutions, serving as a key technical liaison.

This position reports directly to the VP, Product Development, working collaboratively across multiple teams to advance the organization's AI capabilities and strategic objectives. 

Responsibilities:

 AI Solution Development

• Design, develop, and implement AI solutions leveraging unimodal and multimodal Large Language Models (LLMs) and agentic systems.
• Develop and integrate agentic systems using frameworks like CrewAI, LangChain, and LangGraph to enable autonomous decision-making and workflow automation.
• Enhance product ecosystems by incorporating emerging AI technologies to improve functionality and user experience.
• Execute model training, fine-tuning, and optimization of inference engines for high-performance AI systems.
• Ensure technical excellence in AI solution design and implementation.
• Evaluate and integrate emerging AI technologies into existing systems.

Cross-Functional Collaboration

• Serve as primary technical liaison between AI engineering teams and product teams
• Translate technical capabilities into product requirements and user-centric solutions
• Help facilitate communication and alignment across engineering, product, and clinical teams
• Drive collaborative innovation and knowledge sharing

Infrastructure and Optimization

• Optimize cloud-based AI infrastructure for scalability, performance, and reliability.
• Implement best practices for efficient AI system deployment and maintenance, including agentic workflows.

Performance and Quality Assurance

• Establish and maintain rigorous testing and validation protocols for AI models
• Drive continuous improvement in AI model accuracy, efficiency, and robustness.

Monitoring and Observability 

• Implement and manage monitoring solutions for AI systems using tools such as Langfuse, Prometheus, Grafana, or equivalent platforms to ensure operational health and performance.
• Analyze observability data to identify and resolve issues in AI model behavior, agentic system performance, and system reliability.
• Develop proactive monitoring strategies to detect anomalies and ensure reliability of AI deployments.

Compliance and Ethical AI

• Ensure AI solutions adhere to healthcare regulatory standards and data privacy requirements (e.g., HIPAA, GDPR)
• Develop and enforce ethical AI guidelines to mitigate biases
• Uphold high standards for secure handling of sensitive information, including ePHI and PHI, per federal, state, and local regulations.

Requirements:

• Master's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field
• Total 8-12 years experience with minimum 3+ years of technical leadership experience in AI/ML technologies
• Proven track record of delivering complex AI solutions in healthcare or technologically advanced domains
• Deep technical proficiency in:

• • Large Language Model (LLM) architectures
  • Multimodal AI system design
  • Agentic system development using frameworks like CrewAI, LangChain, and LangGraph.
  • Machine learning model training and fine-tuning
  • Cloud computing infrastructure (AWS preferred)
  • AI/ML frameworks and development tools (e.g., TensorFlow, PyTorch, Hugging Face)
  • AI monitoring and observability tools such as Langfuse, Prometheus, Grafana, or equivalent platforms.

• Strong programming skills in Python, C++, or other relevant languages.
• Experience in agile and adaptive project management methodologies
• Exceptional problem-solving and analytical skills, with a focus on delivering scalable solutions.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Collaborative mindset with experience working in cross-functional teams.
• Ability to mentor junior engineers and contribute to team development.
• Knowledge of healthcare technology landscapes and regulatory environments (e.g., HIPAA, GDPR) is a plus.
• Awareness of emerging AI technologies, including agentic systems, MCP, A2A protocol and their potential applications.
• Adaptability to work in fast-evolving technological environments.
• Commitment to adhering to organizational information security policies and protecting sensitive information, including ePHI and PHI, in accordance with Federal, State, and local regulations. 

About Get Well:

Now part of the SAI Group family, Get Well is redefining digital patient engagement by putting patients in control of their personalized healthcare journeys, both inside and outside the hospital. Get Well is combining high-tech AI navigation with high-touch care experiences driving patient activation, loyalty, and outcomes while reducing the cost of care. For almost 25 years, Get Well has served more than 10 million patients per year across over 1,000 hospitals and clinical partner sites, working to use longitudinal data analytics to better serve patients and clinicians. AI innovator SAI Group led by Chairman Romesh Wadhwani is the lead growth investor in Get Well. Get Well’s award-winning solutions were recognized again in 2024 by KLAS Research and AVIA Marketplace. Learn more at Get Well and follow-us on LinkedIn and Twitter.

Get Well is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.

About SAI Group:

• SAIGroup commits to $1 Billion capital, an advanced AI platform that currently processes 300M+ patients, and 4000+ global employee base to solve enterprise AI and high priority healthcare problems.
• SAIGroup - Growing companies with advanced AI; https://www.cnbc.com/2023/12/08/75-year-old-tech-mogul-betting-1-billion-of-his-fortune-on-ai-futu re.html
• Bio of our Chairman Dr. Romesh Wadhwani: Team - SAIGroup (Informal at Romesh Wadhwani - Wikipedia)
• TIME Magazine recently recognized Chairman Romesh Wadhwani as one of the Top 100 AI leaders in the world - Romesh and Sunil Wadhwani: The 100 Most Influential People in AI 2023 | TIME

Role Overview

As a Staff Solutions Engineer, you will play a strategic, hands-on role in transforming JazzX’s AGI platform into scalable, high-impact solutions for enterprise customers. With your deep technical expertise and leadership, you’ll drive the design, development, and deployment of LLM-powered applications across complex client environments.

You will:

• Lead the integration of LLM features, vector search pipelines, and agentic workflows into production systems.
• Partner with solution architects, product managers, and customer success teams across the full solution lifecycle—from discovery through deployment and optimization.
• Influence core platform enhancements by contributing real-world learnings and reusable components.
• Set the technical bar through design leadership, mentoring, and rigorous engineering practices.

Key Responsibilities

Solution Architecture & Implementation

• Architect and implement end-to-end AGI solutions, including retrieval-augmented generation (RAG), tool-using agents, and LLM orchestration patterns.
• Design and integrate secure data pipelines, APIs, and authorization layers tailored to customer environments.
• Make high-impact, component-level design decisions, balancing performance, maintainability, and customer goals.

Delivery Leadership

• Own delivery of proof-of-concepts, pilots, and scalable production rollouts in agile settings.
• Ensure production readiness through observability (logs, metrics, tracing) and contribute to incident response/on-call rotations as needed.

Collaboration & Enablement

• Work closely with research and product teams to test and validate emerging LLM techniques in customer contexts.
• Serve as a technical escalation point for customer implementations, proactively identifying and resolving complex issues.
• Mentor junior engineers and contribute to internal best practices through code reviews, demos, and documentation.

Innovation & Thought Leadership

• Evaluate and pilot emerging frameworks (e.g., AutoGen, LangGraph, WASM inference engines) and contribute reusable assets to the internal platform.
• Shape JazzX’s field engineering playbook by sharing insights, reusable templates, and domain-specific solutions.

Qualifications

Must-Have

• 8+ years of experience in professional software engineering, with at least 3+ years working on ML, data, or AI/LLM-centric systems.
• Strong expertise in Python (or Go/Java), with production-grade coding, testing, and CI/CD best practices.
• Proficiency in modern GenAI/LLM frameworks (e.g., LangChain, LlamaIndex, HuggingFace Transformers, PyTorch, TensorFlow) and vector databases (e.g., Pinecone, Weaviate, FAISS).
• Familiarity with integrating REST/GraphQL APIs, Kafka streams, and relational/non-relational databases.
• Excellent communication skills and the ability to work cross-functionally with engineering, product, and customer stakeholders.
• Willingness to travel up to 20% for key customer engagements.

Nice-to-Have

• Experience delivering RAG, fine-tuning, or agentic solutions in regulated industries (e.g., finance, healthcare, telecom).
• Any Certifications in cloud
• Exposure to MLOps and data engineering tools (e.g., MLflow, Kubeflow, Vertex AI, Airflow, dbt).

Attributes We Value

• Technical Leadership – You take ownership, guide architecture decisions, and raise the bar for engineering quality.
• Customer Focus – You thrive on solving real-world challenges and building trust with technical stakeholders.
• Startup Mindset – You move fast, embrace ambiguity, and iterate quickly.
• Collaboration & Mentorship – You elevate those around you through thoughtful feedback, knowledge sharing, and support.
• Mission-Driven – You’re motivated by the opportunity to shape the future of intelligent, human-centric software.

Senior Staff Engineer

As a Senior Staff Engineer at JazzX AI, you will operate as a strategic technical leader—driving platform architecture, guiding critical projects, and influencing product direction.  You will serve as a hands-on contributor, provide technical mentorship across teams, and partner with senior stakeholders to ensure our solutions are innovative, robust, and aligned with the company’s long-term vision. You will also be instrumental in fostering a high-performance engineering culture that emphasizes collaboration, excellence, and continuous improvement. 

Key Responsibilities 

Technical Leadership & Architecture 

• Define and evolve JazzX.ai’s technical strategy, ensuring our AI-driven systems can scale and adapt to future challenges.
• Own the end-to-end architecture for key platform components, ensuring high levels of reliability, security, and performance.
• Evaluate emerging technologies, tools, and frameworks, making data-driven recommendations that influence company-wide engineering decisions. 

Project Execution & Cross-Functional Collaboration 

• Lead complex, high-impact engineering initiatives that require coordination across multiple product and infrastructure teams.
• Collaborate with Product Management, AI research engineering, , and UX to scope technical requirements and deliver solutions that align with business objectives.
• Serve as a trusted advisor to senior stakeholders, clearly communicating technical roadmaps, trade-offs, and risks. 

Mentorship & Team Development 

• Guide and mentor engineers at all levels, from early-career to senior staff, providing technical direction and career development support.
• Champion best practices in code quality, architecture reviews, testing, and documentation.
• Foster an inclusive and innovative engineering culture, encouraging knowledge-sharing and continuous learning. 

Innovation & AI Integration 

• Partner with the AI research engineering team team to design and implement modern AI frameworks and capabilities  as production services in the platform
• Drive the exploration and adoption of state-of-the-art AI/ML technologies to keep JazzX.ai ahead of industry trends.
• Identify strategic opportunities where AI can deliver enhanced functionality or novel capabilities for our platform. 

Operational Excellence 

• Define and enforce SLAs for mission-critical services, ensuring minimal downtime and fast recovery.
• Champion DevOps best practices around CI/CD, cloud infrastructure, observability, and security.
• Collaborate with Site Reliability Engineers (SREs) and Security teams to optimize performance and maintain compliance with industry standards. 

 Qualifications 

Educational Background 

• Bachelor’s or Master’s in Computer Science, Engineering, or a related field; or equivalent experience. 

Experience & Technical Expertise 

• 10–12+ years of professional software engineering experience, with at least 5 years in technical leadership roles (Principal, Staff, or equivalent).
• Proven track record leading complex, large-scale projects across distributed systems, backend architecture, or cloud platforms.
• Deep expertise in at least one or more programming languages (e.g., Python, Java, Javascript, Go,  C++), with a willingness to learn new technologies.
• Strong understanding of microservices, event-driven architectures, and container orchestration tools (Kubernetes, Docker).
• Experience with large-scale data processing, streaming platforms (e.g., Kafka), and databases (both SQL and NoSQL).
• Familiarity with AI/ML frameworks (TensorFlow, PyTorch) and operationalizing machine learning solutions is a plus. 

AI/ML Experience: 

• Demonstrated experience in designing, building, deploying, and managing Machine Learning (ML) and Artificial Intelligence (AI) platforms and services. Hands on experience with MLOps practices, frameworks (TensorFlow, PyTorch), and tools. 
• Experience building platforms and applications with modern LLMs and AI agent frameworks like LangChain, LlamaIndex , AutoGen etc. is a strong plus 

Leadership & Soft Skills 

• Exceptional communication and presentation skills, with the ability to influence technical and non-technical stakeholders at executive levels.
• Proven ability to mentor, coach, and inspire engineers, build high-performing teams and a culture of engineering excellence.
• Experience navigating ambiguous requirements and delivering results in a fast-paced, startup environment.
• Demonstrated thought leadership in architectural design, code quality, and technical strategy. 

Staff Software Development Engineer  

As a Staff Software Development Engineer at JazzX AI, you'll define the technical vision for our enterprise AI platform, mentor senior engineers, and oversee the design, development, and deployment of critical AI, software and infra services and components.  You'll be responsible for ensuring the platform's scalability, reliability, security and observability. 

What You Will Do 

Design and Architect Scalable Solutions: 

• Lead the design and development of high-performance, scalable services and components for our enterprise AI platform using a cloud-first, cloud-agnostic approach with world class AI capabilities, scalability, security, privacy and operability.
• Champion a cloud-first, cloud-agnostic approach to ensure platform versatility, scalability, and future-proof architecture.
• Champion innovative AI and software design patterns and best practices to ensure code maintainability, reusability, and testability.
• Contribute to the platform's technical direction by staying up to date on emerging AI technologies, cloud design patterns and user experience methodologies. 

Collaborative Innovation: 

• Foster a collaborative environment by working closely with cross-functional teams (AI/research engineers, software engineers, architects, product managers, and designers) to translate complex platform and product requirements into elegant, scalable, and modular AI, software and infrastructure components.
• Foster a collaborative and inclusive environment by effectively communicating technical concepts and design decisions across diverse audiences.
• Provide mentorship and guidance to junior engineers, fostering their growth and development. 

Driving Operational Excellence: 

• Oversee the seamless integration of components into the platform, ensuring efficient operation, reliability, and a dependable user experience for both customers and partners.
• Champion DevOps and AIOps best practices for AI services, maintainability, performance optimization, and observability. 

Requirements 

• Minimum 8+ years of experience designing, building, and operating cloud-native applications at scale. Expertise in container orchestration frameworks like Kubernetes and containerization technologies like Docker. Expertise in AI/ML/data capabilities, platforms, frameworks and tools.
• Proven track record deploying and managing cloud environments using Infrastructure as Code (IaC) tools like Terraform, Ansible, or CloudFormation.
• Experience in designing and implementing highly available, scalable, and fault-tolerant systems. 

Development Lifecycle: 

• End-to-end ownership of the entire software development lifecycle (SDLC) for complex enterprise platforms and products. This includes experience in design, prototyping, development (including unit, integration, and end-to-end testing), deployment, and ongoing operations. 

Technical Skills: 

• Mastery of essential software development principles with a focus on clean code, design patterns, and best practices.
• In-depth expertise in at least two of the following programming languages: Python, Java, JavaScript, Golang, or C++.
• Solid understanding of distributed systems concepts and experience with distributed tracing tools. 

AI/ML Experience: 

• Demonstrated experience in building, deploying, and managing Machine Learning (ML) and Artificial Intelligence (AI) platforms and services. Familiarity with MLOps practices, frameworks (TensorFlow, PyTorch), and tools is a plus 
• Familiarity with modern LLMs and AI agent frameworks like LangChain, LlamaIndex , AutoGen etc. is a strong plus 

We are seeking an experienced and highly skilled Senior Data Scientist to lead our efforts in optimizing ad display on native Android surfaces. In this role, you will design and implement data-driven strategies to maximize ad effectiveness, user engagement, and revenue. You will collaborate closely with product managers, engineers, and cross-functional teams to drive innovation, improve ad relevance, and ensure a seamless user experience.

As a Senior Data Scientist, you’ll get to:

• Develop and refine data-driven ad optimization strategies tailored to native Android environments, focusing on user engagement, personalization, and revenue uplift.
• Design, implement, and improve predictive models for ad targeting, recommendation, and bidding strategies using cutting-edge machine learning and statistical techniques.
• Lead the design and analysis of experiments to test ad placements, formats, and targeting methods. Use data from A/B tests and multivariate testing to validate hypotheses and inform decisions.
• Analyze user behavior patterns to understand engagement and dropout points, leveraging insights to optimize ad positioning, timing, and relevance.
• Partner with product managers, engineers, designers, and marketing teams to align ad strategies with business objectives and user experience goals. Communicate insights and recommendations effectively to both technical and non-technical stakeholders.
• Ensure the scalability of ad models across a wide range of Android devices and varying network conditions. Work closely with engineering teams to deploy and monitor models in a production environment.
• Provide mentorship and guidance to junior data scientists, fostering a collaborative and innovative team environment. Lead by example in developing high-quality, scalable solutions for complex ad optimization challenges.

You’ll be a good fit if you have:

• PhD (preferred) or Master's Degree (required) in Computer Science, Data Science, Statistics, Machine Learning, or a related field.
• 6+ years of experience in data science or machine learning, with a strong focus on ad technology or user experience optimization.
• Demonstrable experience in working with ad serving, ad tech stacks, and optimization techniques, especially within Android or mobile ecosystems.
• Expertise in machine learning frameworks (TensorFlow, PyTorch, or similar).
• Advanced proficiency in Python, or Kotlin.
• Experience with data processing and pipeline tools (e.g., Spark, Hadoop, Airflow).
• Deep understanding of A/B testing, causal inference, and other experimentation methods.
• Familiarity with ad tech, programmatic advertising, and user behavior metrics, particularly in the context of mobile and Android platforms.
• Excellent analytical, problem-solving, and communication skills. Proven ability to articulate complex data science concepts to stakeholders across different domains.

 Nice to have: 

• Previous experience optimizing ad displays on native mobile or Android surfaces.
• Familiarity with the Android SDK and understanding of native Android UI/UX design principles.
• Knowledge of reinforcement learning, multi-armed bandit models, and contextual targeting techniques.

This role is based at our Bengaluru, India office and follows a Hybrid schedule that will be aligned with our Return to Office guidelines.

Overview

You will join the Sailfin Accounts Receivable (AR) Team, a product engineering team working in the Finance and Accounting domain. The team designs, develops, deploys, and maintains a large-scale AR product used by global customers. Most of the team is based in India, including this role in Bangalore, and we work with a modern technology stack—AI/ML models, LLMs, multi-agent systems, cloud platforms, and automation frameworks. Our focus is on building secure, scalable, and intelligent solutions that transform financial operations.

The Role

We are looking for an Applied AI Engineer who can build production-ready predictive, generative, and agentic AI solutions for the Finance domain.

You will handle end-to-end development—from identifying use cases to deploying AI features in production. You must have experience building real-world, production-grade AI and agentic systems, not just POCs.

Responsibilities

• Partner with product owners and finance experts to identify strong AI use cases in Accounts Receivable.
• Design and build predictive models, generative AI features, and multi-agent systems tailored to business problems.
• Develop LLM-based copilots and autonomous agents, including human-in-loop flows, security guardrails, and audit trails.
• Work closely with software engineers and data scientists to embed AI features into the product.
• Build and maintain secure, scalable MLOps pipelines for training, deployment, and monitoring.
• Continuously evaluate and improve model performance and user impact.
• Stay current with the latest advancements in LLMs, NLP, multi-agent frameworks, and applied AI engineering.
• Set up and tailor AI demos as needed for customers, stakeholders, and internal teams.
• Contribute to a culture of innovation, collaboration, and continuous learning.

Required Experience

AI/ML/LLM Experience

• 7+ years of AI/ML engineering experience.
• 3+ years of hands-on experience with LLM-based solutions.
• 1+ year of experience building agentic systems (production-ready, not POCs).

Multi-Agent & Autonomous Systems

• Strong understanding of multi-agent architectures, memory systems, tool-use, orchestration, routing, and safety guardrails.
• Experience building autonomous or semi-autonomous agents with governance, logging, and human-in-loop flows.

Technical Skills

• Strong programming skills in Python (preferred), R, or Java.
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Solid understanding of predictive modeling, NLP, ML algorithms, and statistics.
• Experience working in cloud environments (AWS/Azure/GCP) with model deployment and data pipelines.
• Knowledge of GitHub/GitLab, version control, and CI/CD practices.
• Basic understanding of web fundamentals (HTTP, JSON, authentication) and REST APIs to integrate AI/LLM solutions into the product.

General Skills

• Strong analytical and problem-solving skills.
• Ability to communicate complex concepts clearly to non-technical partners.
• Great collaboration and teamwork abilities.
• Curiosity and passion for innovation in AI and Finance.

Tekion seeks an exceptionally talented and seasoned Staff / Lead Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale.  
 
Key Responsibilities 

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges. 

• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions. 

• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives. 

• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services. 

• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation. 

• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth. 

• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership. 

• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI 

Core Competencies & Technical Expertise 

            The successful candidate will demonstrate mastery in the following areas: 

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI. 

• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data. 
   
   

Deep Learning: 

• Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch. 

• Extensive experience implementing advanced neural network architectures. 

• Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g.,spaCy) methodologies. 

• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications. 

Technical Proficiencies: 

• Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous. 

• Proficiency with cloud computing platforms (GCP, Azure, AWS). 

• Expertise in experimental design (A/B testing, causal inference). 

• Proficient in version control systems (Git). 

Required Qualifications 

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field. 

• A minimum of 7 - 9 years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value. 

• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach. 

• Proven experience in programming, data manipulation, and building models in a business context. 

• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences. 
   

Ideal Candidate Profile 
 

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving. 

• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable. 

• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred. 

• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences. 

• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams. 

• A commitment to continuous personal and team development within a collaborative, high-performance culture. 

Perks and Benefits

• Competitive compensation 

• Generous stock options 

• Medical Insurance coverage 

• Work with some of the brightest minds from Silicon Valley’s most dominant and successful companies 

Glance - An InMobi Group Company    

Founded in 2019, Glance is a consumer technology company that operates some of the most disruptive digital platforms including Glance, Roposo, and Glance TV. Glance has redefined the way the internet is consumed on the lock screen, removing the need to search for and download apps. Over 400 million smartphones now come enabled with Glance’s next-generation internet experience.   
  
Roposo has revolutionized commerce by launching a destination for creator-led live entertainment commerce. Glance TV is changing the way consumers engage and interact with their televisions.   
  
Headquartered in Singapore, Glance is an unconsolidated subsidiary of InMobi Group and is funded by Jio Platforms, Google, and Mithril Capital. For more information, visit glance.com, roposo.com, and inmobi.com.    

  
What should you know about joining Glance?  
 

At Glance, we walk the talk – free yourself, dream big, and chase your passion! On joining, you’ll have opportunities to make an immediate impact on mission-critical projects, as you work with highly capable and ambitious peer groups.   
  

Be rewarded for your autonomy even as you collaborate. Ideate, innovate, and inspire by leveraging bleeding-edge tech to disrupt consumer experiences.   
  

While you work, we’ll take care of nourishing your body, mind, and soul. This includes daily meals, gym, trainings, tech tools, and regular unwind sessions. Also, feel free to bring your kids – even the furry ones – to the office!  

Role overview:

You will drive the vision and strategy for the Recommendation Systems team at Glance and will play a transformational role in driving the larger AI-first aspirations of the company. Recommendations team at Glance plays key role for all Ad revenue – programmatic and direct – on all Glance user experiences. Revenue has been growing steadily, and we’re well placed to make the trajectory steeper. Expertise in designing, building, and operating large-scale recommendation systems is a must.

You will have knowledge of current AI and machine learning capabilities and advances in the field. You should also be interested in the academics of data science but be more focused on practical application within recommendation and personalization technology. You will be able to clearly articulate the purpose of data science solutions to key stakeholders and customers, and then translate those into action for the business. The solutions you and your teams provide will encompass such things as product innovations, create efficiencies and automation across the business, improve data architecture and system architecture, mitigate business risks, and create process improvements. 

We are searching for an individual who thrives on describing a vision and inspiring a team to achieve it. We need a leader who will remove obstacles, break barriers, empower, communicate, and engage. Someone who truly harnesses advanced analytic data modelling systems to drive positive outcomes for our customers. From the defining of a strategy to the execution of it, you will also develop, collect, and report the objective metrics required to assure it. You will own driving employee engagement and increasing productivity across the Data Science team and into Engineering.

The impact you’ll make:

• Define the overall vision for our recommendation applications, focused on up-levelling internal use of machine learning
• Provide technical leadership of overall architecture, ML approaches, performance monitoring, continuing improvement, and production deployments
• Manage, develop, coach, and mentor a team of machine learning engineers and big data specialists
• Partner with our business and product teams to help predict system behaviour, establish metrics, identify bugs, and improve debugging skills
• Ensure data quality and integrity within our products as well as our teams
• Partner with our client-facing teams and customers to enhance products and develop client solutions applying critical thinking skills to remove extraneous inputs
• Conceive, plan, and prioritize data projects
• Lead data mining and collection procedures, especially focused on unstructured and siloed data sets
• Experiment with new models and techniques.
• Drive the implementation of models into Production through various Engineering teams
• Create a positive culture to maximize productivity and minimize attrition

The experience you'll need:

• PhD or Master’s in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research or Economics, Analytics, Data Science. Or Bachelor's with additional experience.
• 12+ years of ML related industry experience working on large-scale recommendation systems or personalization. With 5+ years in a leadership role.
• You would bring deep expertise in the applied algorithms, models, and techniques used to design, scale, and optimize large-scale recommendation systems.
• You should be comfortable with software programming and statistical platforms such as Tensorflow, PyTorch, scikit-learn, etc. etc.
• You should be comfortable with using one or more distributed training technologies such as Apache spark, RAPIDS, dask, etc. along with MLOps stack such as kubeflow, mlflow, or their cloud counterparts
• Comfortable collaborating with cross-functional teams.
• Excellent technical and business communication skills and should know how to present technical ideas in a simple manner to business counterparts.
• Possess a high degree of curiosity and ability to rapidly learn new subjects

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems.

You will design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

• Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
• Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
• Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

• Build and experiment with agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.

Cross-functional impact

• Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance’s consumer experiences.
• Contribute to Glance’s ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.

The experience we need

We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.

You should have experience with:

• Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
• Classical ML and deep learning techniques across NLP, sequence modeling, RL, clustering, and time series.
• Experience in deploying ML workflows/models in production system
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
• Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
• Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Comfortable working in cross-functional teams.
• Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
• Excellent communication skills with the ability to simplify complex technical concepts.

Glance - An InMobi Group Company    

Founded in 2019, Glance is a consumer technology company that operates some of the most disruptive digital platforms including Glance, Roposo, and Glance TV. Glance has redefined the way the internet is consumed on the lock screen, removing the need to search for and download apps. Over 400 million smartphones now come enabled with Glance’s next-generation internet experience.   
  
Roposo has revolutionized commerce by launching a destination for creator-led live entertainment commerce. Glance TV is changing the way consumers engage and interact with their televisions.   
  
Headquartered in Singapore, Glance is an unconsolidated subsidiary of InMobi Group and is funded by Jio Platforms, Google, and Mithril Capital. For more information, visit glance.com, roposo.com, and inmobi.com.   

What should you know about joining Glance?  

At Glance, we walk the talk – free yourself, dream big, and chase your passion! On joining, you’ll have opportunities to make an immediate impact on mission-critical projects, as you work with highly capable and ambitious peer groups.   

Be rewarded for your autonomy even as you collaborate. Ideate, innovate, and inspire by leveraging bleeding-edge tech to disrupt consumer experiences.   

While you work, we’ll take care of nourishing your body, mind, and soul. This includes daily meals, gym, trainings, tech tools, and regular unwind sessions. Also, feel free to bring your kids – even the furry ones – to the office!  

Role overview:

You will drive the vision and strategy for the Commerce Intelligence at Glance  such as Multi Modal Search, Recommendation Systems, Brand and Price Affinity Modeling and will play a transformational role in driving the larger AI-first aspirations of the company. The Commerce Intelligence team at Glance will play a critical role in driving commerce across the platform.

You will have knowledge of current AI and machine learning capabilities and advances in the field. You should also be interested in the academics of data science but be more focused on practical application within recommendation and personalization technology. You will be able to clearly articulate the purpose of data science solutions to key stakeholders and customers, and then translate those into action for the business. The solutions you and your teams provide will encompass such things as product innovations, create efficiencies and automation across the business, improve data architecture and system architecture, mitigate business risks, and create process improvements. 

We are searching for an individual who thrives on describing a vision and inspiring a team to achieve it. We need a leader who will remove obstacles, break barriers, empower, communicate, and engage. Someone who truly harnesses advanced analytic data modelling systems to drive positive outcomes for our customers. From the defining of a strategy to the execution of it, you will also develop, collect, and report the objective metrics required to assure it. You will own driving employee engagement and increasing productivity across the Data Science team and into Engineering.

The impact you’ll make:

• Define the overall vision for our commerce intelligence, focused on up-levelling internal use of machine learning.
• Provide technical leadership of overall architecture, ML approaches, performance monitoring, continuing improvement, and production deployments.
• Manage, develop, coach, and mentor a team of machine learning engineers and big data specialists.
• Partner with our business and product teams to help predict system behaviour, establish metrics, identify bugs, and improve debugging skills.
• Ensure data quality and integrity within our products as well as our teams.
• Partner with our client-facing teams and customers to enhance products and develop client solutions applying critical thinking skills to remove extraneous inputs.
• Conceive, plan, and prioritize data projects.
• Lead data mining and collection procedures, especially focused on unstructured and siloed data sets.
• Experiment with new models and techniques.
• Drive the implementation of models into Production through various Engineering teams
• Create a positive culture to maximize productivity and minimize attrition

The experience you'll need:

• PhD or Master’s in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research or Economics, Analytics, Data Science. Or bachelor's with additional experience.

• 10+ years of ML related industry experience working on large-scale recommendation systems or personalization. With 3+ years in a leadership role.

• You would bring deep expertise in the applied algorithms, models, and techniques used to design, scale, and optimize large-scale recommendation systems.
• You should be comfortable with software programming and statistical platforms such as Tensorflow, PyTorch, scikit-learn, etc. etc.
• You should be comfortable with using one or more distributed training technologies such as Apache spark, RAPIDS, dask, etc. along with MLOps stack such as kubeflow, mlflow, or their cloud counterparts
• Comfortable collaborating with cross-functional teams.
• Excellent technical and business communication skills and should know how to present technical ideas in a simple manner to business counterparts.
• Possess a high degree of curiosity and ability to rapidly learn new subjects
• Experience in e-commerce is plus.

Key Responsibilities

• ML Platform Architecture: Design and scale distributed ML platform for data, feature store, training, and inferencing; build multi-region, containerized infrastructure using Kubernetes and Terraform.
• Training & Inferencing at Scale: Optimize GPU/TPU/CPU utilization for large-scale training and real-time inferencing; implement distributed training, model parallelism, and caching with Ray.
• Performance & Governance: Drive FinOps-aligned architecture, auto-scaling, and efficiency; enable observability, SLA/SLO tracking, and incident management.
• Collaboration & Leadership: Partner with cross-functional teams to deliver high-impact ML solutions; mentor engineers and set best practices for ML systems design.

Technology Stack

• ML Frameworks: TensorFlow, PyTorch
• Serving: Triton, TensorFlow Serving, TorchServe
• Distributed Compute: Ray, Kubernetes, Spark, GPU/TPU optimization
• Lifecycle: MLflow and AirFlow
• Infra: AWS/GCP/Azure, Terraform, CI/CD

What We’re Looking For

• 8–12 years in ML platform/distributed systems.
• Strong in Python, Kubernetes, GPU/TPU optimization.
• Proven ability to design fault-tolerant, high-throughput ML systems.

About Tekion

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform. Our Automotive Retail Cloud (ARC), Automotive Enterprise Cloud (AEC), and Automotive Partner Cloud (APC) connect OEMs, retailers, and consumers through one seamless platform. Utilizing cutting-edge technology, big data, machine learning, and AI, Tekion is transforming the automotive retail ecosystem. We're inventing new technology along the way to overcome barriers and solve big problems, all while having a blast doing it with offices in North America, Asia, and Europe, Tekion employs around 3,000 people worldwide.

Tekion is pioneering the digital transformation of the automotive industry, a sector that has remained largely undisrupted for over five decades. Our flagship platform, the Automotive Retail Cloud™, is the first and fastest cloud-native solution of its kind, fundamentally re-engineering the end-to-end automotive retail experience. By leveraging vast datasets, sophisticated machine learning, and advanced AI, we are creating a seamlessly integrated ecosystem connecting OEMs, retailers, and consumers. We are a globally distributed team of technologists and innovators dedicated to solving complex, high Impact challenges.

The Position

Tekion seeks an exceptionally talented and seasoned Senior Staff Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is a seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale. 

Key Responsibilities

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges.
• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions.
• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives.
• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services.
• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation.
• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth.
• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership.
• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.
• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.
• Deep Learning:
  • Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch.
  • Extensive experience implementing advanced neural network architectures.
  • Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies.
• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications.
• Technical Proficiencies:
  • Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous.
  • Proficiency with cloud computing platforms (GCP, Azure, AWS).
  • Expertise in experimental design (A/B testing, causal inference).
  • Proficient in version control systems (Git).

Required Qualifications

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field.
• A minimum of 11-14 years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value.
• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach.
• Proven experience in programming, data manipulation, and building models in a business context.
• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences.

Ideal Candidate Profile

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving.
• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable.
• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred.
• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences.
• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams.
• A commitment to continuous personal and team development within a collaborative, high-performance culture.

Tekion is pioneering the digital transformation of the automotive industry, a sector that has remained largely undisrupted for over five decades. Our flagship platform, the Automotive Retail Cloud™, is the first and fastest cloud-native solution of its kind, fundamentally re-engineering the end-to-end automotive retail experience. By leveraging vast datasets, sophisticated machine learning, and advanced AI, we are creating a seamlessly integrated ecosystem connecting OEMs, retailers, and consumers. We are a globally distributed team of technologists and innovators dedicated to solving complex, high Impact challenges.

The Position

Tekion seeks an exceptionally talented and seasoned Senior Staff Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is a seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale. 

Key Responsibilities

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges.
• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions.
• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives.
• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services.
• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation.
• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth.
• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership.
• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.
• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.
• Deep Learning:
• Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch.
• Extensive experience implementing advanced neural network architectures.
• Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies.
• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications.
• Technical Proficiencies:
• Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous.
• Proficiency with cloud computing platforms (GCP, Azure, AWS).
• Expertise in experimental design (A/B testing, causal inference).
• Proficient in version control systems (Git).

Required Qualifications

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field.
• A minimum of 11-14 years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value.
• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach.
• Proven experience in programming, data manipulation, and building models in a business context.
• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences.

Ideal Candidate Profile

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving.
• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable.
• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred.
• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences.
• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams.
• A commitment to continuous personal and team development within a collaborative, high-performance culture.

Perks and Benefits

• Competitive compensation
• Generous stock options
• Medical Insurance coverage
• Work with some of the brightest minds from Silicon Valley’s most dominant and successful companies

Role Purpose

We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process.

What You’ll Do

Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition)
Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX
Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps.
Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS.
Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.

What We’re Looking For

Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
Deep expertise in computer vision and biometrics, especially face recognition.
Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code.
Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.
Cloud Native: Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).

Nice to Have

Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
Large Scale Search: Experience with vector databases (e.g., Milvus, Faiss) and approximate nearest neighbor (ANN) search algorithms.
Familiarity with privacy, security, and compliance in biometric systems.
Mobile/Edge Experience: Experience porting models to edge or mobile devices utilizing frameworks such as CoreML, LiteRT, and/or TFLite.
Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training.
Strong communication skills.

Snapshot

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

About Us

The Agents team at Google DeepMind India is driven by the mission of developing foundational capabilities in Large Models towards realizing AGI. We focus on uncovering emergent agentic behaviors through novel reinforcement learning algorithms run at scale.  

We also apply our research advances to Gemini and critical product launches in Google, touching the lives of hundreds of millions of users, and we are looking forward to doing more!

We are seeking a highly motivated and innovative Research Scientist to join our team in Bangalore, focused on advancing the state-of-the-art in autonomous agents  through reinforcement learning and ML optimization methods. You will conduct cutting-edge research on large language models (LLMs), focusing on the development of more capable, robust autonomous agents.

The Role

Research Scientists at Google DeepMind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the world's leading academic and industrial labs in PhDs, post-docs or professorships, Research Scientists join Google DeepMind to work collaboratively within and across Research fields. They develop solutions to fundamental questions in machine learning and AI.

Key responsibilities:

• Design, implement and evaluate models, agents and software prototypes of large foundational models.
• Push the boundary of state of the art RL methods and machine learning optimization methods to build autonomous agents. 
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About You

You are a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI. You embrace change and thrive under ambiguity. You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges. You are passionate about seeing your research translated into real-world products that improve the lives of users and are eager to work in an environment where research has a direct path to product impact.  You are eager to see your research contribute to real-world applications and are driven by a desire to create positive change through AI.

• PhD in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier machine learning conferences or journals.
• Solid understanding of deep learning, natural language processing, computer vision, and/or speech processing.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.

In addition, the following would be an advantage: 

• Experience with multimodal learning, large language models, and/or assistive AI agents.
• Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations.
• Familiarity with large-scale model training and deployment.
• Strong programming skills in Python or similar languages.
• Excellent communication and collaboration skills.

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Role Purpose 

The Machine Learning Engineer III will play a critical role in advancing Jumio's Extraction team's mission to develop and enhance state-of-the-art solutions for data extraction for ID verification purposes. This role is essential for ensuring the highest standards of security and user verification through the application of advanced machine learning and deep learning techniques, ultimately contributing to Jumio's leadership in the online identity verification, eKYC, and AML solutions market

Role Value 

As a Machine Learning Engineer III at Jumio, you will have the opportunity to significantly impact the security and user experience of our ID verification solutions. Your expertise in machine learning, deep learning, and computer vision will drive the development of innovative algorithms that keep Jumio at the forefront of the industry. By deploying and maintaining these models in production, you will ensure the robustness and reliability of our solutions, supporting our clients across diverse industries such as Financial Services, Travel, Sharing Economy, Fintech, and Gaming. Your contributions will be pivotal in maintaining Jumio's reputation as the leading provider of online identity verification solutions, helping to meet the growing demand for secure and seamless user verification globally.

Example Responsibilities

. 

• Design and implement machine learning, deep learning, classical CV and NLP algorithms focused on data extraction.
• Research to support the deployment of advanced algorithms.
• Develop, evolve, and support production-quality code.
• Deploy models as AWS SageMaker endpoints or directly onto devices.
• Stay updated with the latest advancements in deep learning, computer vision, and NLP by engaging with academic papers and attending industry conferences.
• Work collaboratively with other engineers and product managers in an Agile development environment.

Experience and Qualifications 

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• A minimum of 6+ years of commercial experience in the field of computer vision, ML or a related area, not including time spent studying (5 years with Master’s).
• Proficiency in computer vision, deep learning and natural language processing techniques.
• Proficiency in working with LLMs and VLMs.
• Hands-on experience with PyTorch or TensorFlow, OpenCV, SKLearn, and in developing production-grade Python code.
• Demonstrated experience in implementing best practices for monitoring and maintaining ML models in production environments.
• Experience in building, training, and deploying ML models to production environments.
• Strong communication and problem-solving skills.

Great to have Experience and Qualifications 

• PhD in Computer Science or related field with a specialization in computer vision, deep learning or NLP.
• Experience in developing algorithms for OCR, text extraction and text understanding.
• End to end experience (training, deploying, monitoring) with shipping VLM and LLM models in a production environment.
• Understanding of Spring / Java based system architectures.
• Experience with cloud environments - AWS / GCP.