We're seeking a Head of Machine Learning to own and scale the ML function at Prodigal. This is the senior-most ML role in the company — partnering directly with the CTO as a peer technical leader, defining the multi-year ML vision, building and leading a world-class team, and translating cutting-edge research into production systems that move the needle for over 100 customers and millions of consumers. You'll set the bar for technical excellence, hire and develop the next generation of ML leaders, and shape what consumer finance intelligence looks like over the next decade. If you've operated at this level before — or are ready to step into it at a company where ML is the engine of the product — we want to talk.

Responsibilities:

• Own the ML function end-to-end — vision, roadmap, team, delivery, and measurable business impact across classical ML, NLP, GenAI, and Agentic AI
• Partner with the CTO as the senior ML leader to set company-wide technical direction and shape the long-term roadmap for Prodigal's Intelligence Engine
• Build and lead a high-performing ML organization — hire, mentor, and develop ML engineers, applied scientists, and tech leads across multiple pods
• Translate research into product — drive the application of LLMs, Agentic AI, the modern NLP stack, recommendation systems, and classical ML into production systems serving 100+ customers
• Set technical standards for model development, deployment, monitoring, evaluation, and experimentation that scale across the org
• Architect data and ML infrastructure in partnership with the platform and engineering teams to support large-scale training and reliable, low-latency inference
• Communicate strategy and outcomes clearly to the founders, board, customers, and broader engineering organization
• Represent Prodigal externally — at conferences, in research collaborations, and as a senior voice in the consumer finance AI community
• Stay at the frontier of ML and GenAI research, identifying where new techniques unlock product and business advantage
• Collaborate cross-functionally with product, engineering, GTM, and customer success leaders to ensure ML investment ties directly to revenue, retention, and customer outcomes

Requirements:

• 10+ years of experience in machine learning and software development at a tech or product company, with 4+ years leading ML teams (managing engineers and/or tech leads)
• Proven track record of shipping ML products in production at scale with measurable business impact
• Deep technical fluency across classical ML, deep learning, and the modern NLP/LLM stack — you can still go deep in design reviews, not just review at the surface
• Strong coding skills in Python. Hands-on with PyTorch, scikit-learn, Pandas, and Spark
• Demonstrated success hiring, mentoring, and growing ML talent — including team leads and senior engineers
• Strong executive communication and the ability to set technical direction with founders, peers, and the board
• A bias for outcomes — you connect model work to revenue, engagement, and operational metrics

Preferred Qualifications:

• Experience as a founding ML hire, ML lead, or applied science leader at a high-growth AI/ML product company
• Background in voice AI, conversational AI, speech, NLP on unstructured text, or fintech/lending domains
• Experience with cloud services (AWS), Databricks, and building scalable, distributed systems
• Track record of running A/B testing and experimentation programs at scale
• Patents, publications, or open-source contributions; strong institutional pedigree (IIT, BITS, NIT, IISc)
• Experience scaling an ML org through a hypergrowth phase

Job Benefits

• GenAI at the core – Lead the ML function at a company where AI isn't a feature, it's the product. Shape groundbreaking work that redefines consumer finance intelligence.
• World-class team - You'll get the chance to learn from (and teach) some of the brightest and most skilled people you'll ever meet. Our team members have been part of BCG, Deloitte, EY, Blackstone, Meta, Amazon and are IIT, IIM, BITS alumnus.
• Continuous education - As relentless seekers of knowledge, we sponsor and support any training materials, books, courses, and exam fees for upskilling yourself in areas related to your role - but that's not where it ends. If you make a good case for some extra learning, we're delighted to foot the tab.
• Food at the office – Meals are on us. We won't let your stomach grumble while you hustle.
• Health insurance - Health always comes first. The health of your family is as important to us as it is to you. We offer insurance coverage for both you and your family.
• Flexible schedule - We're not a "clock in, clock out" company. Morning person? Start work while the world's still sleeping. Night owl? Ramp things up while others are winding down. You're fully trusted to create the right conditions for your own personal peak productivity. We only ask that you be available to your teammates for seamless collaboration.
• Generous leave policy - Take all the time you need to recharge your batteries - no cap on vacation here. Because a rested, relaxed, and refreshed mind is a happy and effective mind.
• Recreation & team activities – We regularly indulge in friendly football, table tennis, cooking sessions, and challenge each other to do those extra push-ups or chug that 4th drink (of course, that's Nimbu Pani).

Meet Sangram (Cofounder and CTO) and hear his vision for Prodigal: https://www.youtube.com/embed/FTWlN9fwiIA?feature=shared

About Inizio Ignite

We are Inizio Ignite, the global advisory partner for health and life sciences encompassing connected expertise across Research Partnership, Putnam, STEM, and Vynamic. Guided by purpose, passion, and precision, we collaborate with clients to ignite impact, accelerate performance, and deliver lasting change for patients. By uniting strategy, insights, and innovation with a unique depth and breadth of expertise, we drive transformation and shape the future of health. Proudly part of Inizio – the market-leading commercialization partner for health and life sciences. From early-stage development to loss of exclusivity, we simplify complexity, unlock value, and make collaboration seamless at every pivotal moment – helping healthcare leaders shape strategy and drive meaningful change. 

About the Role

We are seeking an experienced Data Science Manager to join our team at Inizio Ignite. The ideal candidate will bring deep machine learning expertise, strong GenAI and cloud competencies, and hands-on experience delivering analytical solutions that drive meaningful business impact for life sciences clients.

This leader will manage data scientists, oversee project delivery, partner with cross-functional teams, and shape the development of innovative AI/ML solutions.

What you'll be doing

Leadership & Delivery

• Lead and manage a team of data scientists, providing technical guidance, mentorship, and performance oversight.
• Own end-to-end delivery of data science and GenAI engagements for pharma/biotech clients.
• Translate business problems into analytical frameworks and ensure high-quality, timely project execution.
• Drive adoption of AI/ML capabilities across client engagements and internal initiatives.

Technical Responsibilities

• Architect, design, and review ML, statistical, and GenAI solutions, ensuring scalability, accuracy, and alignment with business objectives.
• Oversee the development, validation, and deployment of predictive models, NLP pipelines, and GenAI-based applications (e.g., summarization, insights automation, generative workflows).
• Lead cloud-based solution deployment (AWS, Azure, or GCP) including orchestration, MLOps best practices, and integration with enterprise systems.
• Ensure adherence to model governance, compliance, and responsible AI frameworks.

What you'll need

• Extensive experience in data science or advanced analytics roles, with at least 2–3 years in a leadership or managerial capacity.
• Ideally prior experience in pharmaceutical, biotech, or healthcare consulting is mandatory.
• Strong expertise in machine learning, NLP, deep learning, and GenAI (LLMs, prompt engineering, RAG, fine-tuning, embeddings).
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP; exposure to MLOps frameworks preferred.
• Proficiency in Python, SQL, and common DS/ML libraries (TensorFlow, PyTorch, scikit-learn, Hugging Face, LangChain, etc.).
• Proven experience managing teams, delivering client projects, and presenting to senior stakeholders.
• Good to have understanding of pharma datasets (claims, RWE, clinical, medical insights, social data) and key use cases.
• Experience building GenAI solutions in production environments

Our Pledge

At Inizio, we value inclusivity, recognize the power of diversity, and inspire the next generation of change-makers. We are an equal opportunities employer and are committed to creating a dynamic work environment that values diversity, equity, and inclusion. We welcome all applications regardless of race, color, religion, gender identity, age, national origin, marital status, veteran status, genetic information, sexual orientation, or disability.

Artificial Intelligence

Inizio may use AI technologies to support certain aspects of our recruitment process, but all hiring decisions are made by our human recruiting team. We are committed to fair, inclusive, and human-led hiring practices. Don't meet every requirement? That's okay! We are dedicated to building a diverse, inclusive, and authentic workplace. If you're excited about this role but your experience doesn't perfectly align with every qualification, we encourage you to apply anyway.

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)

Who we are

Metropolis is an artificial intelligence company that uses computer vision technology to enable frictionless, checkout-free experiences in the real world. Today, we are reimagining parking to enable millions of consumers to just "drive in and drive out." We envision a future where people transact in the real world with a speed, ease and convenience that is unparalleled, even online. Tomorrow, we will power checkout-free experiences anywhere you go to make the everyday experiences of living, working and playing remarkable - giving us back our most valuable asset, time.

Who you are

We are looking for a highly motivated and analytical Data Scientist to join our growing data team. You will play a key role in extracting insights from large datasets, building predictive models, and supporting data-driven decision-making across departments.

What you'll do

• Collect, process, and analyze large datasets from multiple sources
• Build and deploy machine learning models to solve business problems
• Design and implement A/B tests and statistical analyses
• Collaborate with cross-functional teams (product, engineering, marketing) to define analytics requirements
• Communicate complex data insights in a clear and actionable manner to stakeholders
• Develop dashboards and visualizations to monitor key metrics
• Stay current with the latest trends and technologies in data science and AI

What we're looking for

• Bachelor’s and or Master’s degree in Computer Science, Mathematics, Statistics, or related field
• 4+ years of proven experience as a Data Scientist or Data Analyst
• Strong knowledge of Python/R and SQL
• Hands-on experience with machine learning frameworks (e.g., scikit learn, tensroflow, pytorch)
• Experience with big data tools (e.g., Spark, Hadoop)
• Familiarity with data visualization tools
• Strong analytical, problem-solving, and communication skills

While not required, these are a plus:

• Experience with cloud platforms preferably AWS (S3, Sagemaker, Airflow etc)
• Strong SQL skills, with experience in Snowflake, MySQL, and PostgreSQL
• Familiarity with data visualization tools (e.g., Tableau, Power BI, Looker)

3 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection.

When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. #LI-SR1 #LI-Onsite

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.

Job Summary

As a Data Scientist, you will work within a cross-functional agile team contributing to the company's growth in a fast-moving environment. You will develop new tools and features by working closely with engineers, product managers, designers, and engineers. Ultimately, the goal is to help our users make smarter decisions. On top of that, you’ll have the support of a diverse, large team of data scientists working across all Brandwatch products to support your technical and career development. This role is open to people with a variety of professional and academic backgrounds and you can expect to develop your skills in a wide variety of techniques, including machine learning, statistical modelling, and computational linguistics.

Essential Duties and Responsibilities

Work with stakeholders to clarity and refine questions.

Collect, clean, and prepare data, generally this will be large volumes of text along with metadata and will often need to be handled with big data tools.

Build models to solve a range of different problems, such as text classification and clustering, automated insight generation, and time series analysis.

Develop evaluation methodologies, which will vary from those based around conventional classification metrics (such as precision and recall), to designing methods to evaluate how well your tool helps a user achieve a defined task.

Support engineers as your projects are shipped. Sometimes you will also help maintain and upgrade the tools and technologies you develop.

Perform other duties as required to support evolving business needs.

Minimum Required Qualifications

A good working knowledge of Python (or another modern programming language, with willingness to learn Python).

A solid understanding of statistics and/or machine learning.

Enjoy an experimental and iterative approach to building products.

An ability to communicate with non-technical stakeholders, or willingness to skill up in this area.

Preferred Qualifications

Working in a data science team.

Common data science libraries, such as Numpy, Scipy, Pandas, SKlearn, Spacy.

Big data tools, such as: Spark, Dask, MapReduce, or alternatives.

Cloud computing, for example: AWS EC2, S3.

Linux style shell commands.

Application prototyping frameworks such as Dash, or Shiny.

As a Staff Data Scientist, you will act as a senior technical leader and trusted advisor across multiple teams. You’ll guide stakeholders through the complexity of modern AI systems, with a particular focus on agentic AI architectures (including LangGraph, LangFuse, and related tooling), as well as broader machine‑learning strategy. This role blends deep technical expertise, consultative problem‑solving, and cross‑functional leadership. 

You will often support several initiatives simultaneously, helping teams understand feasibility, scope, and risk while shaping high‑impact AI solutions that align with business needs. You’ll also mentor junior colleagues, elevate data‑science practices, and contribute hands‑on to the development of production‑grade AI systems. 

What you'll do:

AI Consulting & Advisory 

• Partner with product, engineering, and business teams to translate ambiguous problems into clear AI opportunities. 

• Provide expert guidance on agentic AI, including workflow orchestration, evaluation, observability, and safety. 

• Assess the complexity, feasibility, and expected value of proposed AI initiatives, helping teams prioritise effectively. 

• Communicate trade‑offs, risks, and solution paths to both technical and non‑technical audiences. 

AI & ML Solution Design 

• Architect end‑to‑end AI systems that solve real business problems, from early discovery through deployment. 

• Design agent‑based workflows, retrieval‑augmented pipelines, and ML‑powered automation. 

• Define evaluation strategies, data requirements, and success metrics for AI features and products. 

• Collaborate with engineering teams to ensure solutions are scalable, maintainable, and aligned with platform strategy. 

Hands‑On Technical Work 

• Build prototypes, proof‑of‑concepts, and production‑ready components using Python, modern AI frameworks, and cloud tooling. 

• Develop and refine agentic AI workflows using LangGraph, LangChain, LangFuse, or similar frameworks. 

• Implement machine‑learning models, retrieval systems, and data pipelines where needed. 

• Contribute to internal libraries, tooling, and best practices for AI development. 

Leadership & Mentorship 

• Mentor junior and mid‑level data scientists, providing guidance on modelling, experimentation, and system design. 

• Lead technical discussions, design reviews, and cross‑team collaboration sessions. 

• Champion high standards for code quality, documentation, and reproducibility. 

• Help shape the long‑term AI strategy and technical roadmap, through your technical knowledge on the art of the possible, and the business problems our clients face. 

What you'll bring:

• Extensive experience in data science, machine learning, or applied AI, including hands‑on coding in Python and common data science libraries, such as Numpy, Scipy, Pandas, SKlearn, Spacy. 

• Ability with prototyping frameworks such as Dash. 

• Good working understanding of code version control with Git. 

• Experience with big data tools, such as: Spark, Dask, or alternatives. 

• Strong understanding of agentic AI, orchestration frameworks, and LLM‑based system design. 

• Ability to evaluate project complexity, identify risks, and guide teams toward pragmatic solutions. 

• Experience designing and delivering AI systems in production environments. 

• Excellent communication skills and the ability to influence beyond your immediate team. 

• Great mentoring skills. 
• Comfort working across multiple projects and shifting priorities. 

Nice to Have 

• Experience with observability and evaluation tools such as LangFuse, Weights & Biases, or similar. 

• Familiarity with agentic evals and prompt‑engineering patterns. 

• Background in consulting, technical advisory, or cross‑functional leadership roles. 

• Experience with cloud platforms (AWS, GCP, Azure) and modern MLOps practices. 

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.

What are we looking for?

SentinelOne is seeking a Software Engineer to join the Observo AI team in India, our cutting-edge AI-driven data pipeline optimization platform. This role will be responsible for developing and maintaining high-performance systems that process massive volumes of telemetry data while reducing costs and improving insights for enterprise customers.

We're looking for someone who errs on the side of simplicity, who has a passion for craftsmanship in code, and who believes deeply in the value of testing. You should be the kind of engineer who writes tests even when nobody is watching—because you understand that quality matters. We value engineers who enjoy performance tuning, extracting the last ounce of efficiency from systems, and who are excited about working with databases, distributed systems, and open source technologies.

This role is part of the Observo AI engineering organization and offers the opportunity to work on innovative technology that directly impacts enterprise data management at scale. This is a hybrid role with 3 days in our Bengaluru office.

What Will You Do?

• Develop and maintain scalable data processing pipelines that handle large volumes of telemetry data (logs, metrics, traces)
• Build and enhance ML-driven data routing, filtering, and transformation components to optimize customer data volumes while preserving critical insights
• Implement real-time analytics and monitoring features using modern software engineering practices
• Work with cloud-native microservices and APIs that integrate with major observability platforms (Splunk, Elastic, Datadog, New Relic)
• Contribute to monitoring, alerting, and observability solutions for distributed systems
• Write comprehensive tests for all code changes, ensuring reliability and maintainability
• Participate in code reviews, pair programming, and collaborative development practices
• Collaborate with senior engineers, Product, and DevOps teams to deliver customer-focused solutions
• Conduct performance and scale testing to optimize system efficiency
• Learn and apply emerging technologies in AI/ML, data engineering, and observability
• Contribute to technical documentation and knowledge sharing within the team

What Skills and Knowledge Should You Bring?

• 3+ years of software engineering experience with focus on backend development, distributed systems, or data processing
• Demonstrated commitment to code quality and testing practices throughout your career
• Experience that shows you care about the craft of software engineering
• Strong proficiency in Go, Java, Python, or similar programming languages with understanding of software engineering best practices
• Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes)
• Familiarity with data processing frameworks such as Apache Kafka, Apache Spark, or streaming technologies
• Basic understanding of database technologies including SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) systems
• Understanding of data formats and protocols commonly used in enterprise systems (JSON, REST APIs, message queues)
• Experience with version control systems (Git) and CI/CD practices
• Strong belief in testing—you write tests even when you have the freedom to skip them
• Bias toward simplicity and maintainability in software design
• Interest in performance and scale testing; you enjoy tuning systems for maximum efficiency
• Passion for craftsmanship in code quality and attention to detail
• Commitment to continuous learning and improvement
• Good communication skills with ability to collaborate effectively in a team environment
• Experience with or interest in pair programming and extreme programming practices
• Fluency in English with good written and verbal communication skills
• Eagerness to learn from senior engineers and contribute to team success
• Bachelor's degree in Computer Science, Engineering, or related field from a recognized institution
• Exposure to functional programming languages (Haskell, Scala, Elixir, OCaml, F#)
• Exposure to systems programming languages (Rust, C++, C)
• Contributor to open source projects, particularly in databases, distributed systems, or observability
• Experience with machine learning concepts and frameworks (TensorFlow, PyTorch, scikit-learn)
• Knowledge of monitoring and observability tools such as Prometheus, Grafana, or ELK stack
• Personal projects demonstrating passion for databases, distributed systems, or performance optimization

Why Us? 

You will be joining a cutting-edge company, where you will tackle extraordinary challenges and work with the very best in the industry

• Flexible working hours and hybrid/remote work model.
• Flexible Time Off.
• Flexible Paid Sick Days.
• Global gender-neutral Parental Leave (16 weeks, beyond the leave provided by the local laws) 
• Generous employee stock plan in the form of RSUs (restricted stock units)
• On top of RSUs, you can benefit from our attractive ESPP (employee stock purchase plan)
• Gym membership/sports gears by Cultfit.
• Wellness Coach app, with 3,000+ on-demand sessions, daily interactive classes, audiobooks, and unlimited private coaching. 
• Private medical insurance plan for you and your family.
• Life Insurance covered by S1 (for employees)
• Telemedical app consultation (Practo)
• Global Employee Assistance Program (confidential counseling related to both personal and work life matters)
• High-end MacBook or Windows laptop.
• Home-office-setup allowances (one time) and maintenance allowance. 
• Internet allowances.
• Provident Fund and Gratuity (as per govt clause)
• NPS contribution (Employee contribution)
• Half yearly bonus program depending on the individual and company performance.
• Above standard referral bonus as per policy.
• Udemy Business platform for Hard/Soft skills Training & Support for your further educational activities/trainings
• Sodexo food coupons.

Opportunity Overview

As the Manager, Machine Learning & Data Science, you will play a key role in building and scaling Cohere Health’s AI capabilities in India. You’ll lead a team of machine learning engineers and data scientists focused on developing and deploying models that automate and augment complex clinical and administrative workflows.

This team is responsible for the full lifecycle of applied machine learning and data science—from problem framing and experimentation to production model deployment and ongoing optimization. You’ll work with structured and unstructured healthcare data to generate insights and power intelligent systems that improve prior authorization and broader clinical decision-making.

As a player-coach, you’ll combine hands-on technical contributions with team leadership, helping to establish best practices, mentor team members, and drive high-impact solutions in close partnership with Product, Engineering, Clinical, and Analytics stakeholders. This is an opportunity to shape both the technical direction and team culture within a growing global organization.

What you’ll do:

• Lead, mentor, and develop a team of machine learning engineers and data scientists, fostering a collaborative, high-performance environment
• Act as a hands-on contributor across the ML/DS lifecycle, including data exploration, feature engineering, model development, evaluation, and deployment
• Design, develop, and deploy machine learning models for retrieval, classification, and generative use cases across structured and unstructured data
• Translate complex business and clinical problems into scalable machine learning and data science solutions
• Establish and uphold best practices for experimentation, model validation, performance tracking, and reproducibility
• Partner cross-functionally with Product, Engineering, Clinical, and Analytics teams to align solutions with business priorities
• Guide the development of scalable data science and machine learning systems, including data preprocessing pipelines and production workflows
• Monitor model performance, identify opportunities for improvement, and drive continuous iteration and optimization
• Communicate technical concepts, methodologies, and insights clearly to both technical and non-technical stakeholders
• Contribute to hiring and scaling the team in India, including recruiting, onboarding, and coaching team members 

What you’ll need:

Must-haves

• Minimum 8+ years of experience in machine learning, data science, or applied AI roles, of those a minimum 2+ years of experience must be leading technical teams
• Strong hands-on experience building, evaluating, and deploying machine learning models in production
• Solid foundation in statistical methods, experimental design, and model evaluation
• Proficiency in Python and experience with ML frameworks (e.g., PyTorch, scikit-learn)
• Experience working with large, complex datasets (structured and/or unstructured)
• Strong problem-solving skills with the ability to translate business challenges into analytical solutions
• Excellent communication skills, with the ability to present complex concepts clearly to diverse stakeholders
• Experience working in fast-paced, evolving environments

Nice-to-haves

• Experience in healthcare, particularly with payer, provider, or clinical data
• Experience with NLP, deep learning (e.g., transformers), or generative AI (e.g., RAG)
• Familiarity with MLOps practices and production ML systems
• Experience with Spark or large-scale data processing frameworks
• Exposure to cloud platforms (e.g., AWS, SageMaker)
• Experience working in a global or distributed team environment
• Prior experience in a GCC or building teams in India

Interview Process*:

1. Connect with Talent Acquisition 
2. Meet with the Hiring Manager
3. Behavioral Interview(s)
4. Case Study
5. Interview with Senior Leadership

*Subject to change

About Cohere Health:

Cohere Health’s clinical intelligence platform delivers AI-powered solutions that streamline access to quality care by improving payer-provider collaboration, cost containment, and healthcare economics. Cohere Health works with over 660,000 providers and handles over 12 million prior authorization requests annually. Its responsible AI auto-approves up to 90% of requests for millions of health plan members.

With the acquisition of ZignaAI, we’ve further enhanced our platform by launching our Payment Integrity Suite, anchored by Cohere Validate™, an AI-driven clinical and coding validation solution that operates in near real-time. By unifying pre-service authorization data with post-service claims validation, we’re creating a transparent healthcare ecosystem that reduces waste, improves payer-provider collaboration and patient outcomes, and ensures providers are paid promptly and accurately.

Cohere Health’s innovations continue to receive industry wide recognition. We’ve been named to the 2025 Inc. 5000 list and in the Gartner® Hype Cycle™ for U.S. Healthcare Payers (2022-2025), and ranked as a Top 5 LinkedIn™ Startup for 2023 & 2024. Backed by leading investors such as Deerfield Management, Define Ventures, Flare Capital Partners, Longitude Capital, and Polaris Partners, Cohere Health drives more transparent, streamlined healthcare processes, helping patients receive faster, more appropriate care and higher-quality outcomes.

The Coherenauts, as we call ourselves, who succeed here are empathetic teammates who are candid, kind, caring, and embody our core values and principles. We believe that diverse, inclusive teams make the most impactful work. Cohere is deeply invested in ensuring that we have a supportive, growth-oriented environment that works for everyone.

We can’t wait to learn more about you and meet you at Cohere Health!

Equal Opportunity Statement: 

Cohere Health is an Equal Opportunity Employer. We are committed to fostering an environment of mutual respect where equal employment opportunities are available to all.  To us, it’s personal.

The Role

Lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.

This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.

At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.

What It Means to Be L5 at Securly

L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.

• Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
• Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
• Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
• Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
• Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
• Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
• Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.

What You'll Do

• Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
• Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
• Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
• Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
• Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
• Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
• Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
• Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
• Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
• Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.

Skills & Requirements

Who You Are

• You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
• You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
• You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
• You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
• You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
• You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.

About Securly

Benefits

The Role

The Senior Data Scientist will lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.

This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.

At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.

What It Means to Be L5 at Securly

L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.

• Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
• Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
• Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
• Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
• Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
• Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
• Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.

What You'll Do

• Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
• Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
• Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
• Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
• Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
• Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
• Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
• Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
• Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
• Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.

Skills & Requirements

Who You Are

• You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
• You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
• You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
• You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
• You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
• You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.

About Securly

Benefits

Rhombus Power India is purposefully transforming the nation’s defense and national security enterprises with Guardian, its Artificial Intelligence platform for strategic, operational, and tactical decision-making at the speed of relevance.

We are looking for people who are driven by a sense of purpose. In this role, you will serve as a Data Engineer to solve our customer’s uniquely complex problems. We expect you to be a systems-level thinker and a real doer. You will excel in this role if you are hungry to make a difference and are intellectually bold. And we sure hope that you smile when you hear our clients say, “If we can dream it, Rhombus can do it.”

Come join our cross-disciplinary and world-class team of technologists who are working hard every day to deliver game-changing solutions to transform national security.

Location

New Delhi, India

Job Description

Rhombus’ Data Engineers are instrumental in designing and implementing data engineering activities on topical, mission-driven projects that inform our national security agenda. They analyze, build, and maintain our organization’s data infrastructure and processing systems. As a Data Engineer, you’ll perform various code development and query needs for integration with new and existing applications, dashboards and machine learning pipelines. You’ll work cross functionally with other departments and utilize your skills at all stages of the analytics pipeline to create results that drive business values and customer success! You’ll be asked to travel to client sites where you’ll engage and collaborate directly with the client to discuss data pipelines, solutioning, and to ensure a strong client connection.

Responsibilities:

• Develop code using various programming and scripting languages to automate data ingestion and improve data management processes.
• Architect data repositories, stand up data platforms and develop data pipelines for ingestion, transformation, and aggregation.
• Review existing architecture, data strategy, and improve processes for data governance, data quality, and metadata management.
• Extract and analyze raw data from multiple data sources via APIs, SQL Stored Procedures, or Python scripts.
• Ability to develop scripts and programs for converting various types of data into usable formats and support project team to scale, monitor and operate data platforms.
• Collaborate with a multi-disciplinary team of analysts, data scientists, data engineers, developers, and data consumers in a fast-paced, agile environment.
• You’ll use your experience in analytical exploration and data examination while you manage the assessment, design, building, and maintenance of scalable platforms for clients.
• Communicate project status and results to various levels of leadership.

Qualifications:

• A Bachelor’s degree in Data Analytics, Computer Science, Computer Engineering, Information Systems/Sciences, or other relevant area (or equivalent experience) and at least 1 year of professional experience, or a Master’s degree with strong academic project experience.
• Experience with 1 or more programming and scripting languages, with a strong focus on Python, Pandas and Numpy. Other languages include: Shell, PERL, Java, C/C++/C#, Scala, etc.
• Experience with 1 or more of the following relational, noSQL and/or file based storage (e.g. MYSQL, MongoDB, SQL Server, Oracle, Postgres, Hbase, DynamoDB, etc.)
• Experience building and maintaining ETL data pipelines
• Experience with software development life cycle including testing, documenting, delivery and support
• Working knowledge of AWS/cloud technologies
• Experience using query optimization as well as data modeling techniques.
• Familiarity with machine learning frameworks (such as TensorFlow, Scikit-Learn, etc.)

Personal Qualities:

• Ability to contextualize data as it relates to Rhombus’ vision. We want someone who can understand the “why” and “so what”, and not just the “how”. Our Data Engineers help us transmit the enthusiasm we have around the data work to the individuals doing that work.
• Must have strong problem-solving skills and demonstrated excellent oral and written communication skills.
• We’re seeking folks with strong intellectual curiosity and creativity, with an ability and willingness to learn new skills and apply novel solutions to problems.
• Comfort in a fast-paced start-up environment is crucial, with the ability to consistently revise your approach in response to new information, and to be flexible and adaptive.

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: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

Company Overview

At CommerceIQ, we help consumer brands accelerate their retail ecommerce market share growth and profitability through machine learning algorithms. We are building the world’s most complete and sophisticated Retail Ecommerce Management Platform, which connects and intelligently automates the management of retail ecommerce channels like Amazon, Walmart, and Instacart, across the entire ecommerce operational chain of retail media management, sales operations, supply chain, and digital self analytics.

We are in hyper growth mode, having recently raised our Series D funding at unicorn valuation (>$1B) and ended our third year of triple-digit revenue growth. Continued acceleration of our growth is fueled by landing new customers, expanding our platform through new products, managing new retail ecommerce platforms, and delivering exceptional customer service to unlock high net retention rates.

What You’ll Be Doing

As a Lead ML Engineer in the Data Science team, you will work closely with the engineering, product, and operations teams to build state-of- the-art ML based solutions for B2B SaaS products. This will entail applying advanced ML algorithms at scale for core products and developing robust end to end production pipelines which includes Human-in-the-Loop component to boost the quality. 

In this role, you will find innovative ways to apply emerging technologies to improve business processes and drive more value for customers. The ideal candidate will have a strong background in machine learning model development, natural language processing and understanding, and data analysis which they can utilize to manage and improve our company’s AI/ML initiatives. 

• Architect and Own multiple AI driven end to end pipelines that allows for deployment and scalability of machine learning models
• Build tools and capabilities that help with data ingestion to feature engineering, data management and organization
• Build tools and capabilities for scalability through distributed optimization 
• Deploy  cutting edge algorithms like LLMs etc. on GPUs at scale
• Work with stakeholders on the research and software engineering side of the company to understand how to support their teams
• Build tools and capabilities for model management and model performance monitoring
• Build operating services at scale with high availability and reliability
• Propose and implement the best engineering practices for scaling ML-powered features, with a goal to enable the fast iteration of and efficient experimentation with novel features
• Contribute and influence the ML infrastructure roadmap in collaboration with Data Science team

What we are looking for -

• Bachelor’s or Masters in Computer Science or Maths/Stats from a reputed college with 5+ years of experience in solving of experience in machine learning engineering problems 
• Experience and understanding of the entire machine learning pipeline from data ingestion to production
• Experience with machine learning operations, software engineering, and architecture
• Experience with large-scale systems including parallel computing and GPUs
• Experience architecting and building an AI pipelines that supports productionization of ML models
• Experience with MLOps systems
• Strong programming skills in a scientific computing language such as Python, SQL
• Experience using frameworks for machine learning and data science like scikit-learn, pandas, NumPy
• Experience working with ML tools such as Tensorflow, Keras, and Pytorch
• Ability to take successful, complex research ideas from experimentation to production
• Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams
• Good depth and breadth in machine learning (theory and practice), optimization methods, data mining, statistics and linear algebra

Why you will love to work with us -

We have multiple brands, which are among the biggest brands operating on amazon, as our customers. You will be responsible for working with their data and developing models and algorithms to drive lift in tune of hundreds of thousands of dollars.

• Opportunity to work on cutting edge technologies in text and images data like LLMs, OCR, transformer based embeddings models
• Deploy code using the most sophisticated tech stack like AWS, GCP etc.
• You will own projects end to end i.e., from exploration to final production
• You will be empowered to make decisions. It implies more responsibilities but high learning
• Immense opportunities to learn and develop ML based SaaS products for brands and get company-wide recognition
• Forget endless meetings. Get things done and start making an impact from day 1
• Work hard, have amazing fun, create history, build the future of eCommerce 
• Start-up environment with a strong culture and values to accelerate your career 

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)

Senior Machine Learning Engineer

Location: (Bangalore or Noida) 

The proliferation of machine log data has the potential to give organizations unprecedented real-time visibility into their infrastructure and operations. With this opportunity comes tremendous technical challenges around ingesting, managing, and understanding high-volume streams of heterogeneous data

As a Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic AI systems that reason over massive, heterogeneous log data. You’ll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.

You’ll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.

Responsibilities

• Design, implement, and optimize agentic AI components, including context engineering, memory management, and prompts.
• Collaborate cross-functionally with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.
• Operate autonomously in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.
• Ensure reliability, performance, and observability of deployed agents through rigorous testing and continuous improvement.
• Maintain a strong bias for action—delivering incremental, well-tested improvements that directly enhance customer experience.

Required Qualifications

• B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
• 4-6 years of hands-on industry experience with demonstrable ownership and delivery.
• Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
• Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
• Working knowledge of LLM core concepts, prompt design, and agentic design patterns.
• Strong communication skills and a passion for shaping emerging AI paradigms.

Desired Qualifications

• Prior experience building and deploying AI agents or LLM applications in production.
• Familiarity with modern agentic AI frameworks (e.g., LangGraph, LangChain, CrewAI).
• Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
• Exposure to LLM Ops - infrastructure optimization, observability, latency, and cost monitoring.

About Us

Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.

Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.

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)

The below role is for our Bengaluru office. 

Director – Data Science 

Why Join Sigmoid? 

• Sigmoid provides the opportunity to push the boundaries of what is possible by seamlessly combining technical expertise and creativity to tackle intrinsically complex business problems and convert them into straight-forward data solutions. 
• Despite being continuously challenged, you are not alone. You will be part of a fast-paced diverse environment as a member of a high-performing team that works together to energize and inspire each other by challenging the status quo 
• Vibrant inclusive culture of mutual respect and fun through both work and play

Roles and Responsibilities: 

• Convert broad vision and concepts into a structured data science roadmap, and guide a  team to successfully execute on it. 
• Handling end-to-end client AI & analytics programs in a fluid environment. Your role will be a  combination of hands-on contribution, technical team management, and client interaction.
• Proven ability to discover solutions hidden in large datasets and to drive business results  with their data-based insights 
• Contribute to internal product development initiatives related to data science.
• Drive excellent project management required to deliver complex projects, including  effort/time estimation. 
• Be proactive, with full ownership of the engagement. Build scalable client engagement level  processes for faster turnaround & higher accuracy 
• Define Technology/ Strategy and Roadmap for client accounts, and guides implementation  of that strategy within projects 
• Manage the team-members, to ensure that the project plan is being adhered to over the  course of the project 
• Build a trusted advisor relationship with the IT management at clients and internal accounts  leadership. 

Mandated Skills: 

• A B-Tech/M-Tech/MBA from a top tier Institute preferably in a quantitative subject
• 15+ overall experience with 12+ years of hands-on experience in applied Machine Learning, AI and analytics
• Experience of scientific programming in scripting languages like Python, R, SQL, NoSQL, Spark with ML tools & Cloud Technology (AWS, Azure, GCP) 
• Experience in Python libraries such as numpy, pandas, scikit-learn, tensor-flow, scrapy, BERT etc.
• Strong grasp of depth and breadth of machine learning, deep learning, data mining, and statistical concepts and experience in developing models and solutions in these areas
• Expertise with client engagement, understanding complex problem statements, and offering solutions in the domains of Supply Chain, Manufacturing, CPG, Marketing etc. 

Desired Skills: 

• Deep understanding of ML algorithms for common use cases in both structured and  unstructured data ecosystems. 
• Comfortable with large scale data processing and distributed computing 
• Providing required inputs to sales, and pre-sales activities 
• A self-starter who can work well with minimal guidance
• Excellent written and verbal communication skills

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: Senior Full Stack Machine Learning Engineer

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are looking for a talented and experienced Senior Full Stack Machine Learning Engineer to join our dynamic team.

Job Description:

• As a Senior Full Stack Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying machine learning models that drive our healthcare solutions. You will work closely with data scientists, software engineers, and product managers to build scalable and robust machine learning systems. Your expertise will help us transform healthcare data into actionable insights, ultimately improving patient care and operational efficiency.

Key Responsibilities:

• Model Development: Design, implement, and optimize machine learning models for various healthcare applications, including predictive analytics, natural language processing, and generative AI.
• End-to-End Deployment: Develop and maintain the full lifecycle of machine learning solutions, from data preprocessing and model training to deployment and monitoring in production environments.
• Data Engineering: Collaborate with data engineers to build and maintain data pipelines, ensuring the availability of high-quality data for training and inference.
• Software Development: Write clean, efficient, and maintainable code for machine learning applications, ensuring seamless integration with existing systems.
• Performance Optimization: Continuously monitor and improve the performance of machine learning models and systems, addressing issues related to scalability, latency, and accuracy.
• Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
• Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of continuous learning and innovation.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
• Experience: Minimum of 5 years of experience in machine learning, with a proven track record of deploying models in production environments.

Technical Skills:

• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in building applications leveraging generative AI technologies which includes LLM’s, prompt engineering, Vector Databases, RAG architectures and transfer learning is a plus.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Strong knowledge of data structures, algorithms, and software engineering best practices.
• Familiarity with big data technologies (e.g., Hadoop, Spark) and data pipeline tools (e.g., Airflow, Kafka).
• Experience with frontend and backend development, including frameworks such as React, Node.js, and Django.

Domain Knowledge: Understanding of healthcare data standards (e.g., HL7, FHIR) and regulations (e.g., HIPAA) is a plus.

Soft Skills:

• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.
• Ability to work independently and as part of a team in a fast-paced environment.

What We Offer:

• Competitive salary and benefits package.
• Opportunity to work on impactful projects that improve healthcare outcomes.
• Collaborative and innovative work environment.
• Professional development and growth opportunities.
• Flexible work arrangements, including hybrid and remote options.

Join us in our mission to transform healthcare through technology! We look forward to hearing from you.

WHO YOU ARE

You are a passionate Python and AI/ML Engineer minimum 4 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.

WHAT YOU'LL DO

1. Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
2. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
3. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
4. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
5. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
6. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED 

Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN

Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning

NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch

API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger

Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs

GOOD TO HAVE

Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure

TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js

MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD

Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes.

Role overview

 As an MLOps Engineer, you will design, build, and operate the infrastructure and tooling that power end-to-end ML workflows on AWS, including SageMaker, Bedrock, and Snowflake Cortex. You will partner closely with data scientists, ML engineers, and platform teams to ensure models are reliable, secure, and scalable in production.

Key responsibilities

• Design, implement, and maintain CI/CD pipelines for ML models and data workflows using AWS-native services and infrastructure-as-code.
• Operationalize models built on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and monitoring.
• Build and manage data pipelines and feature stores using services such as AWS Glue, Lambda, Step Functions, and Snowflake.
• Implement observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and establish SLOs/SLAs for production ML services.
• Automate environment provisioning, configuration, and dependency management across dev, test, and production.
• Partner with security and compliance teams to ensure ML workloads meet healthcare, privacy, and regulatory standards (e.g., HIPAA).
• Collaborate with ML engineers and data scientists to productionize notebooks and prototypes into robust, maintainable services.
• Contribute to best practices, standards, and documentation for ML platform and operations across the organization.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
• 4+ years of experience in software engineering, data engineering, or ML engineering with at least 2+ years focused on MLOps or ML platform work.
• Strong proficiency with Python and experience integrating ML libraries or frameworks (e.g., scikit-learn, TensorFlow, PyTorch) into production workflows.
• Hands-on expertise with AWS services relevant to MLOps: SageMaker, Bedrock, IAM, CloudWatch, ECR, ECS/EKS or Lambda, S3, Step Functions, and Glue.
• Experience with Snowflake (including Snowflake Cortex), SQL, and building secure, performant data pipelines into and out of Snowflake.
• Proficiency with CI/CD tools (e.g., GitHub Actions, GitLab CI, CodePipeline) and infrastructure-as-code (e.g., Terraform, CloudFormation, CDK).
• Familiarity with containerization and orchestration (Docker, Kubernetes) and event streaming tools (e.g., Kafka) is a plus.
• Knowledge of software engineering best practices, including testing, code reviews, version control, and design for reliability and scalability.
• Experience in regulated domains or with healthcare data standards and regulations is a plus (e.g., HIPAA, FHIR, HL7).

Soft skills and benefits

• Excellent problem-solving and analytical skills with a focus on reliability and automation.
• Strong communication and collaboration abilities, including working cross-functionally with engineering, data science, and product teams.
• Ability to work independently in a fast-paced environment
• Competitive salary and benefits package.
• Opportunity to work on impactful ML platforms that improve healthcare outcomes.
• Collaborative, innovative environment with professional development and growth opportunities.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (10+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

SLSQ127R200

As a Partner Solutions Architect (PSA) for India, you will work with the technical and sales team members who work directly with our customers. You will develop 'technical champions' within our top Partners, providing enablement on technical matters related to the Databricks product. Working with our partners, you will help our customers to achieve tangible data-driven outcomes through the use of our Databricks Lakehouse Platform, helping data teams complete projects and integrate our platform into their enterprise Ecosystem. As a member of our team, you will exercise and develop expertise in those areas, using open-source projects such as Apache Spark™, MLflow, and Delta Lake; and major public cloud infrastructure and services. the product. You will use this expertise to become a trusted advisor to C&SI partners.

The impact you will have:

• Provide partners with the level of enablement they need to assist their clients in evaluating and adopting Databricks including hands-on Apache Spark™ programming and integration with the wider cloud ecosystem
• Engage with the partner technical community by leading workshops, seminars, and meet-ups
• You will be a Big Data Analytics expert on aspects of architecture and design and will share this with our partner network
• Show expertise by producing creative technical solutions and blog posts

What we look for:

• 8 years of customer facing experience working with external clients or partners across a variety of industry markets
• Core strength in either data engineering or data science
• Experience demonstrating technical concepts, including presenting and whiteboarding
• Experience developing architectures within a public cloud (AWS, Azure, or GCP)
• Hands on experience in SQL, Python, Scala, or Java
• Expertise in at least one of the following:
• Data Engineering technologies (Ex: Apache Spark™, Hadoop, Kafka)
• Data Warehousing (Ex: SQL, OLTP/OLAP/DSS)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, HPO)

Job Description

We are looking for a Senior Data Scientist with a strong foundation in SQL/Python programming, data handling, and data wrangling to work on high-impact projects in the healthcare domain. This is a hands-on role involving advanced analytics, developing metrics and reports from large, complex datasets, and building scalable data solutions like solution workbenches.

The ideal candidate will demonstrate a strong aptitude for understanding business context, generating insights from data, and a proactive attitude toward learning and applying relevant data tools and techniques. A solid grasp of claims analytics, combined with the ability to work in the healthcare space and apply a wide range of quantitative and analytical methods to drive better business decisions, is essential. Experience or interest in Generative AI tools to enhance analytics workflows will be a key advantage.

Responsibilities

• Lead and contribute to custom projects focused on claims-based reporting, Patient Journeys, Market Assessment and predictive modeling using SQL/Python.
• Perform advanced data wrangling, transformation, and analysis on complex healthcare datasets, primarily in the claims domain.
• Create data-driven deliverables and visualizations to support customer engagements and business decision-making.
• Partner with Product and Customer Success teams to deliver insightful, tailored analyses to clients.
• Collaborate with global and cross-functional teams to design and implement optimized data solutions.
• Develop scalable tools such as solution workbenches and automated reporting systems.
• Scope, prioritize, and independently handle client requests with minimal supervision.
• Leverage SQL, Python, and Generative AI tools to drive efficiency, innovation, and automation in analytics workflows.
• Contribute to internal initiatives, knowledge sharing, and continuous improvement of team processes.
• Mentor and guide junior analysts, promoting best practices in coding, modeling, and data handling.

Qualifications

Bachelors or Masters degree in computer science, Statistics, Data Science, or a related
quantitative field.
• 3–5 years of hands-on experience in data science or analytics roles, with a strong focus on SQL
and Python.
• Proven expertise in healthcare data, particularly claims analytics; familiarity with pharma datasets
is mandatory.
• Strong programming skills in Python, including data manipulation, wrangling, and analysis using
libraries such as pandas, NumPy, and scikit-learn.
• Advanced proficiency in SQL for working with large, complex relational datasets.
• Comfortable working with large-scale data and able to assess existing processes to identify
opportunities for improvement.
• Sound knowledge of statistics, data modeling, and predictive analytics.
• Proficient in Microsoft Excel and experience creating clear, impactful presentations of analytical
findings.
• Strong interpersonal, communication, and presentation skills, with the ability to explain technical
concepts to non-technical stakeholders.
• Self-starter with the ability to work independently and collaboratively in a fast-paced, dynamic
environment.

ISMS Responsibilities

• Comply with information security policies and processes
• Report security weaknesses, events or potential events, or other security risks, to the organization
• Execute all tasks related to information security management system as appropriate

Why we love Analytical Wizards, and why you will too!

• Industry leading products
• Work hard, and have fun doing it
• Incredibly fast growth means limitless opportunity
• Flexible and dynamic culture
• Work alongside some of the most talented and dedicated teammates
• A collaborative and friendly culture with very high employee engagement

Job Description

We are looking for a Senior Data Scientist with a strong foundation in SQL/Python programming, data handling, and data wrangling to work on high-impact projects in the healthcare domain. This is a hands-on role involving advanced analytics, developing metrics and reports from large, complex datasets, and building scalable data solutions like solution workbenches.

The ideal candidate will demonstrate a strong aptitude for understanding business context, generating insights from data, and a proactive attitude toward learning and applying relevant data tools and techniques. A solid grasp of claims analytics, combined with the ability to work in the healthcare space and apply a wide range of quantitative and analytical methods to drive better business decisions, is essential. Experience or interest in Generative AI tools to enhance analytics workflows will be a key advantage.

Responsibilities

• Lead and contribute to custom projects focused on claims-based reporting, Patient Journeys, Market Assessment and predictive modeling using SQL/Python.
• Perform advanced data wrangling, transformation, and analysis on complex healthcare datasets, primarily in the claims domain.
• Create data-driven deliverables and visualizations to support customer engagements and business decision-making.
• Partner with Product and Customer Success teams to deliver insightful, tailored analyses to clients.
• Collaborate with global and cross-functional teams to design and implement optimized data solutions.
• Develop scalable tools such as solution workbenches and automated reporting systems.
• Scope, prioritize, and independently handle client requests with minimal supervision.
• Leverage SQL, Python, and Generative AI tools to drive efficiency, innovation, and automation in analytics workflows.
• Contribute to internal initiatives, knowledge sharing, and continuous improvement of team processes.
• Mentor and guide junior analysts, promoting best practices in coding, modeling, and data handling.

Qualifications

Bachelors or Masters degree in computer science, Statistics, Data Science, or a related
quantitative field.
• 3–5 years of hands-on experience in data science or analytics roles, with a strong focus on SQL
and Python.
• Proven expertise in healthcare data, particularly claims analytics; familiarity with pharma datasets
is mandatory.
• Strong programming skills in Python, including data manipulation, wrangling, and analysis using
libraries such as pandas, NumPy, and scikit-learn.
• Advanced proficiency in SQL for working with large, complex relational datasets.
• Comfortable working with large-scale data and able to assess existing processes to identify
opportunities for improvement.
• Sound knowledge of statistics, data modeling, and predictive analytics.
• Proficient in Microsoft Excel and experience creating clear, impactful presentations of analytical
findings.
• Strong interpersonal, communication, and presentation skills, with the ability to explain technical
concepts to non-technical stakeholders.
• Self-starter with the ability to work independently and collaboratively in a fast-paced, dynamic
environment.

ISMS Responsibilities

• Comply with information security policies and processes
• Report security weaknesses, events or potential events, or other security risks, to the organization
• Execute all tasks related to information security management system as appropriate

Why we love Analytical Wizards, and why you will too!

• Industry leading products
• Work hard, and have fun doing it
• Incredibly fast growth means limitless opportunity
• Flexible and dynamic culture
• Work alongside some of the most talented and dedicated teammates
• A collaborative and friendly culture with very high employee engagement

Want to build the next generation of AI-powered analytics and intelligent agents for observability and security use cases? Passionate about leveraging cutting-edge machine learning, large language models, and GPU-accelerated inference to transform how organizations understand and act on their logs data? Come talk with us!

As a Senior Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic AI systems that reason over massive, heterogeneous log data. You’ll combine machine learning, prompt engineering, context engineering and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.

You’ll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.

Location: Noida/ Bengaluru (Hybrid)

Responsibilities

• Design, implement, and optimize agentic AI components, including tools, memory management, and prompts.
• Develop and maintain golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.
• Prototype and evaluate novel prompting strategies and reasoning chains for model reliability and interpretability.
• Collaborate cross-functionally with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.
• Operate autonomously in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.
• Ensure reliability, performance, and observability of deployed agents through rigorous testing and continuous improvement.
• Maintain a strong bias for action—delivering incremental, well-tested improvements that directly enhance customer experience.

Required Qualifications

• B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
• 6+ years of hands-on industry experience with demonstrable ownership and delivery.
• Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
• Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
• Working knowledge of LLM core concepts, prompt design, and agentic design patterns.
• Experience with distributed systems and dealing with large amounts of data.
• Strong communication skills and a passion for shaping emerging AI paradigms.

Desired Qualifications

• Experience leading a team of engineers.
• Prior experience building and deploying AI agents or LLM applications in production.
• Familiarity with modern agentic AI frameworks (e.g., LangGraph, LangChain, CrewAI).
• Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
• Exposure to LLM Ops - infrastructure optimization, observability, latency, and cost monitoring.

About Us

Sumo Logic, Inc. empowers the people who power modern, digital business. Sumo Logic enables customers to deliver reliable and secure cloud-native applications through its Sumo Logic SaaS Analytics Log Platform, which helps practitioners and developers ensure application reliability, secure and protect against modern security threats, and gain insights into their cloud infrastructures. Customers worldwide rely on Sumo Logic to get powerful real-time analytics and insights across observability and security solutions for their cloud-native applications. For more information, visit www.sumologic.com.

Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.

ABOUT THE ROLE

You are a seasoned Data Scientist, passionate about building robust, real-time machine learning solutions to combat financial crime. Working within the Risk & Fraud Team, you will leverage advanced statistical modeling and machine learning to detect, prevent, and adapt to evolving fraud threats. You thrive on solving complex problems with highly imbalanced data and delivering tangible risk reduction impact in a fast-paced environment.

You strongly believe in agile principles and use them to deliver value incrementally. You are eager to learn about new methodologies and practices and apply them directly in your work.

As a Senior Data Scientist you’ll be:

• Model Development & Risk Mitigation: Design, develop, and implement advanced ML/Statistical models to predict and prevent various fraud typologies. Focus on driving impact by optimising costs and reducing risk using ML/Stats models.
• Specialised Data Handling: Employ advanced techniques to manage and model highly imbalanced, large datasets characteristic of the fraud domain.
• Adaptive Systems: Build and maintain models that can quickly adapt to new and evolving fraud typologies and emerging threats.
• Real-Time MLOps: Develop, optimise, and deploy ML models for real-time/low-latency inference in production settings.
• Data & Feature Engineering: Utilize, enrich, and contribute to the feature store ecosystem, ensuring high-quality, scalable features for risk models.
• Performance Monitoring: Implement and maintain robust drift monitoring and model performance tracking to ensure deployed models remain effective and stable.
• Collaboration: Work closely with ML Engineers to productionize models leveraging AWS/GCP tech stack, and collaborate with Business and Product teams to understand requirements and solutionise data products.
• Exploration: Identify creative solutions in order to build relevant training data sets and explore the value of new data structures like knowledge graphs for relational fraud detection.

WHAT ARE WE LOOKING FOR

• You have 6+ years of experience in Software Development or Machine Learning, with a strong emphasis on practical model development and deployment.
• You are Strong in Python and have expert knowledge with Machine Learning tools/libraries (e.g., scikit-learn, statmodels, TensorFlow, Keras, or PyTorch).
• You have experience with big-data technologies such as Spark, SparkML, Hadoop etc. and have handled large data in a distributed processing environment. AWS, GCP, or Azure (anyone) cloud knowledge is preferred.
• You are proficient in ML training, optimising, feature selection and engineering, hyperparameter tuning and have knowledge about trade-offs in using different algorithms.
• You have demonstrated experience in developing and deploying a variety of ML-algorithms (e.g., logistic regressions, boosted trees, neural networks) that work in real-time settings.
• You have a good working knowledge with basic data table operations (SQL etc.), and data transform in the warehouse (dbt etc.).
• You are comfortable with ambiguity and are a self-starter—you take initiative in spotting opportunities and finding ways to solve challenges with data.
• Familiarity with Financial Services and Fintech space is preferred.
• You will be helping optimise all aspects of our sales funnel including finding ways to increase lead quality, reduce our cost of acquisition, optimise lead allocation, and improve the likelihood of successful lending outcomes for our members

Highly Desirable Skills:

• Exposure to Cost-Sensitive Learning techniques, where the economic impact of prediction errors is a key optimization factor.
• A decent idea and practical exposure to MLOps principles (e.g., model versioning, continuous integration/deployment for ML).
• Experience with Drift monitoring tools and frameworks.
• Exposure to Knowledge Graphs or graph database applications.

• Competitive Compensation -  competitive salary and share options
• Time Off – Generous annual leave on top of bank holidays.
• Parental Leave – Paid maternity, paternity, and adoption leave to support your family journey.
• Sabbatical – Extended unpaid and paid leave options after completing milestone years with Tide.
• Health Insurance – Private family insurance with additional OPD coverage and top-up options.
• Life & Accident Cover – Comprehensive accidental and life insurance protection.
• Mental Wellbeing – Access to therapy sessions, courses, meditations, and workshops.
• Volunteering & Development Days – Paid days annually for volunteering or personal growth.
• Learning & Development – Annual budget for books, courses, coaching, and more.
• WOO (Work Outside the Office) – Work from abroad for up to 90 days annually.
• Home Office Setup – Contribution towards setting up your home office
• Laptop Ownership – Keep your old laptop and get a new one when it’s time for a replacement.
• Snacks & Meals – Office perks with snacks, coffee, tea, and lunch (location dependent).

TIDEAN WAYS OF WORKING

At Tide, we champion a flexible workplace model that supports both in-person and remote work to cater to the specific needs of our different teams. 

While remote work is supported, we believe in the power of face-to-face interactions to foster team spirit and collaboration. Our offices are designed as hubs for innovation and team-building, where we encourage regular in-person gatherings to foster a strong sense of community.

ABOUT THE ROLE

You are a seasoned Data Scientist, passionate about building robust, real-time machine learning solutions to combat financial crime. Working within the Risk & Fraud Team, you will leverage advanced statistical modeling and machine learning to detect, prevent, and adapt to evolving fraud threats. You thrive on solving complex problems with highly imbalanced data and delivering tangible risk reduction impact in a fast-paced environment.

You strongly believe in agile principles and use them to deliver value incrementally. You are eager to learn about new methodologies and practices and apply them directly in your work.

As a Senior Data Scientist you’ll be:

• Model Development & Risk Mitigation: Design, develop, and implement advanced ML/Statistical models to predict and prevent various fraud typologies. Focus on driving impact by optimising costs and reducing risk using ML/Stats models.
• Specialised Data Handling: Employ advanced techniques to manage and model highly imbalanced, large datasets characteristic of the fraud domain.
• Adaptive Systems: Build and maintain models that can quickly adapt to new and evolving fraud typologies and emerging threats.
• Real-Time MLOps: Develop, optimise, and deploy ML models for real-time/low-latency inference in production settings.
• Data & Feature Engineering: Utilize, enrich, and contribute to the feature store ecosystem, ensuring high-quality, scalable features for risk models.
• Performance Monitoring: Implement and maintain robust drift monitoring and model performance tracking to ensure deployed models remain effective and stable.
• Collaboration: Work closely with ML Engineers to productionize models leveraging AWS/GCP tech stack, and collaborate with Business and Product teams to understand requirements and solutionise data products.
• Exploration: Identify creative solutions in order to build relevant training data sets and explore the value of new data structures like knowledge graphs for relational fraud detection.

WHAT ARE WE LOOKING FOR

• You have 6+ years of experience in Software Development or Machine Learning, with a strong emphasis on practical model development and deployment.
• You are Strong in Python and have expert knowledge with Machine Learning tools/libraries (e.g., scikit-learn, statmodels, TensorFlow, Keras, or PyTorch).
• You have experience with big-data technologies such as Spark, SparkML, Hadoop etc. and have handled large data in a distributed processing environment. AWS, GCP, or Azure (anyone) cloud knowledge is preferred.
• You are proficient in ML training, optimising, feature selection and engineering, hyperparameter tuning and have knowledge about trade-offs in using different algorithms.
• You have demonstrated experience in developing and deploying a variety of ML-algorithms (e.g., logistic regressions, boosted trees, neural networks) that work in real-time settings.
• You have a good working knowledge with basic data table operations (SQL etc.), and data transform in the warehouse (dbt etc.).
• You are comfortable with ambiguity and are a self-starter—you take initiative in spotting opportunities and finding ways to solve challenges with data.
• Familiarity with Financial Services and Fintech space is preferred.
• You will be helping optimise all aspects of our sales funnel including finding ways to increase lead quality, reduce our cost of acquisition, optimise lead allocation, and improve the likelihood of successful lending outcomes for our members

Highly Desirable Skills:

• Exposure to Cost-Sensitive Learning techniques, where the economic impact of prediction errors is a key optimization factor.
• A decent idea and practical exposure to MLOps principles (e.g., model versioning, continuous integration/deployment for ML).
• Experience with Drift monitoring tools and frameworks.
• Exposure to Knowledge Graphs or graph database applications.

• Competitive Compensation -  competitive salary and share options
• Time Off – Generous annual leave on top of bank holidays.
• Parental Leave – Paid maternity, paternity, and adoption leave to support your family journey.
• Sabbatical – Extended unpaid and paid leave options after completing milestone years with Tide.
• Health Insurance – Private family insurance with additional OPD coverage and top-up options.
• Life & Accident Cover – Comprehensive accidental and life insurance protection.
• Mental Wellbeing – Access to therapy sessions, courses, meditations, and workshops.
• Volunteering & Development Days – Paid days annually for volunteering or personal growth.
• Learning & Development – Annual budget for books, courses, coaching, and more.
• WOO (Work Outside the Office) – Work from abroad for up to 90 days annually.
• Home Office Setup – Contribution towards setting up your home office
• Laptop Ownership – Keep your old laptop and get a new one when it’s time for a replacement.
• Snacks & Meals – Office perks with snacks, coffee, tea, and lunch (location dependent).

TIDEAN WAYS OF WORKING

At Tide, we champion a flexible workplace model that supports both in-person and remote work to cater to the specific needs of our different teams. 

While remote work is supported, we believe in the power of face-to-face interactions to foster team spirit and collaboration. Our offices are designed as hubs for innovation and team-building, where we encourage regular in-person gatherings to foster a strong sense of community.

About the role

We are seeking a Senior Machine Learning Engineer in the Hyderabad area to design, build, and scale machine learning systems that power personalized and data-driven experiences across our digital entertainment ecosystem — including paid VOD streaming, Manga reading, Merchandising, Mobile Gaming, and Music Video platforms. The role will be reporting to the Manager of Machine Learning.

You will work at the intersection of user behavior, content intelligence, and product engagement, building models and systems that both enhance user experience (e.g., personalization, discovery, lifecycle optimization) and generate actionable business insights and services for internal stakeholders. As a senior individual contributor, you will own high-impact initiatives end-to-end and play a key role in shaping the technical direction of ML systems across products.

Core Areas of Responsibility

• Design, develop, and deploy scalable machine learning models that enhance personalization, discovery, engagement, and monetization across multiple product verticals.
• Lead end-to-end ML projects from problem framing and data exploration to production deployment and monitoring.
• Build and optimize robust data pipelines and feature engineering workflows for large-scale behavioral and content data.
• Design and execute rigorous experimentation frameworks (e.g., A/B testing, offline evaluation) to measure model impact.
• Collaborate closely with Product, Engineering, Analytics, and business stakeholders to translate product goals into ML solutions.
• Improve system reliability, scalability, and performance of production ML services.
• Mentor junior engineers and contribute to raising the team’s technical standards and best practices.
• Contribute to the evolution of ML architecture, tooling, and MLOps practices across the organization.

About You

Required Qualifications

• 8+ years of experience building and deploying machine learning systems in production environments
• Strong foundation in machine learning algorithms, statistics, and model evaluation techniques
• Experience working with large-scale user behavior or content datasets
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, xgboost)
• Experience with distributed data processing and data pipeline technologies
• Strong understanding of experimentation methodologies and performance measurement
• Ability to operate independently and drive ambiguous problems to impactful solutions

Preferred Qualifications

• Experience with recommendation systems, ranking models, personalization, or user lifecycle modeling
• Experience in subscription-based digital products, streaming media, gaming, or e-commerce platforms
• Familiarity with cloud infrastructure and production ML services
• Experience optimizing models for real-time or near real-time user-facing applications
  
  

What Success Looks Like

• ML systems you build measurably improve user engagement, retention, or monetization
• Models are reliable, scalable, and well-integrated into product workflows
• Stakeholders trust your technical judgment and rely on your work to inform product direction
• You elevate the team’s engineering rigor and contribute meaningfully to long-term ML platform evolution

About the Team

You will join a fast growing team of Data Scientists, Machine Learning Engineers, and AI Engineers, united by a passion for leveraging data and AI to create transformative solutions. Our team delivers both consumer-facing product features that enhance the streaming experience for millions of users and stakeholder-oriented solutions that empower internal teams with actionable insights. From building personalized recommendation systems to developing innovative tools like our stakeholder-facing chatbot, we combine cutting-edge machine learning, robust data pipelines, and advanced AI to drive impact across the organization. Collaboration, creativity, and a commitment to excellence define our team’s culture as we work together to push the boundaries of what’s possible in streaming and data-driven decision-making.

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…..

WHO YOU ARE

You are a passionate Python and AI/ML Engineer minimum 4 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.

WHAT YOU'LL DO

1. Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
2. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
3. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
4. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
5. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
6. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED 

Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN

Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning

NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch

API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger

Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs

GOOD TO HAVE

Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure

TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js

MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD

Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes

Role Overview

We are seeking a Principal Applied Scientist to be the highest-level AI technical authority on the team. This role is above the Applied Scientist level — you set the AI research and engineering direction, own the agentic system architecture, define the eval framework standards, and operate as the primary interface to the external AI research community and internal platform AI leadership. You will combine deep research rigor with relentless production focus: every AI capability you design ships, gets measured, and improves based on evidence.

You are the person who answers: "Is this AI approach the right one, and how do we know when it's working"

Key Responsibilities

• Own the agentic system architecture: define the multi-agent coordination patterns, MCP tool taxonomy, intent-to-skill routing logic, policy enforcement design, and memory management strategy across all AI workflows on the platform.
• Lead the AI evaluation framework: design the golden dataset structure, define eval metrics (RAG retrieval quality, scoring accuracy, LLM response correctness, agentic workflow success rate), and own the CI/CD AI eval pipeline that gates every AI capability release.
• Drive the RAG architecture strategy: chunking strategies for MongoDB and Elasticsearch-indexed documents, embedding model selection and fine-tuning, retrieval reranking design, and hallucination mitigation patterns.
• Own the LLM integration architecture: prompt engineering standards, LLM gateway usage policies, context window management, streaming response patterns, and model version governance.
• Lead applied research initiatives: identify novel techniques (RLHF, Constitutional AI, multi-modal, sparse retrieval) that are practically applicable to automotive retail intelligence use cases and prototype them rigorously in Python.
• Define the hexagonal AI adapter contract standard: the formal interface specification between Python AI services and Java domain cores — ensuring AI infrastructure remains swappable without domain logic changes.
• Act as primary technical interface to platform AI leadership (LLM Gateway, ML Platform, Agentic Engine) — influencing platform roadmap based on teams practical requirements.
• Mentor the Applied Scientist and ML Architect; conduct technical reviews of AI system designs, eval frameworks, and research approaches.
• Publish, present, or contribute to open-source where appropriate — representing Tekion's AI engineering excellence externally.

Skills & Qualifications

• 10+ years of applied AI/ML experience with at least 3 years at Staff or Principal level — a track record of shipping AI systems that measurably changed business outcomes.
• Deep expertise across: LLM fine-tuning and alignment, RAG system design (chunking, embedding, retrieval, reranking), agentic frameworks (LangGraph, CrewAI, AutoGen), and MCP tool design.
• Python mastery: PyTorch, Hugging Face Transformers, LangChain/LangGraph, scikit-learn, and production ML serving tooling.
• Strong understanding of distributed AI systems: Elasticsearch-backed vector search, Redis/Aerospike serving, Cosmos DB for AI event logging, and Kafka-based feature pipelines.
• Proven ability to design formal eval frameworks for AI systems — not just accuracy metrics but business aligned quality gates.
• Experience defining and enforcing hexagonal adapter contracts between AI services and domain service layers in production systems.
• Track record of communicating complex AI system design to non-technical stakeholders, influencing product roadmaps, and driving cross-team alignment. 

Good to Have

• Publications or significant open-source contributions in NLP, information retrieval, or agentic systems.
• Experience with Constitutional AI, RLAIF, or preference optimization techniques (DPO, PPO).
• Familiarity with multi-modal AI and its application to automotive retail data (images, documents, tabular data fusion).
• Prior experience in a product-focused AI role at a SaaS or platform company — not purely research. 

Perks & Benefits

• Competitive compensation and generous stock options.
• Medical insurance coverage.
• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Here’s a summary of the role:

Ready to build real-world AI solutions that actually make an impact?

You’ll take cutting-edge AI and GenAI concepts—like copilots, agents, and intelligent workflows—and turn them into scalable, production-ready systems used across sales, finance, customer success, and more. If you enjoy hands-on backend engineering, working with APIs, cloud platforms, and LLMs, and transforming ideas into high-performing AI-driven products, this role puts you right at the center of innovation.

Here’s a breakdown of what you’ll do (not all of it, just the important stuff):

• Build and scale backend services, APIs, and integrations that embed AI/GenAI into enterprise systems (CRM, ERP, BI)
• Develop LLM-powered solutions such as RAG pipelines, copilots, agents, and automation workflows
• Take AI solutions from concept to production—design, build, test, deploy, and monitor
• Collaborate with architects and business stakeholders to turn requirements into practical AI implementations
• Create and manage data pipelines to enable reliable, high-quality AI workflows
• Ensure solutions are secure, scalable, observable, and aligned with best practices

These are the essentials you’ll need to get an interview:

• 5+ years of experience in software or solutions engineering with production systems
• Strong programming skills in Python, Java, C#, or Node.js
• Hands-on experience with APIs, integrations (REST/webhooks), and backend development
• Practical experience working with LLMs/GenAI tools (e.g., OpenAI, LangChain, or similar)
• Experience with cloud platforms (preferably AWS), CI/CD, and containerization
• Solid understanding of AI/GenAI concepts like prompt engineering, RAG, or agents
• Ability to translate technical solutions into business impact and collaborate across teams

It would be great if you had these too, but we’ll support you if you don’t:

• Experience integrating AI into CRM, ERP, or analytics platforms
• Familiarity with MLOps/LLMOps concepts such as model lifecycle and monitoring
• Exposure to machine learning or data analytics tools (e.g., Scikit-learn, dashboards)
• Experience working in global or distributed teams
• Knowledge of AI governance, security, and responsible AI practices

About HG Insights

Headquartered in Santa Barbara, California, HG Insights is the global leader in technology intelligence. We help the world’s most innovative companies accelerate their go-to-market efforts with precision through advanced data science methodologies and proprietary data assets. We offer a culture that blends innovation, collaboration, and growth, where each team member is empowered to make a measurable impact.

Role Overview

We are looking for a Staff/Senior Machine Learning Engineer to join our growing AI and Data Platform team in Pune, India. In this role, you will be responsible for designing, building, and scaling ML systems that power our core data intelligence products. You’ll work at the intersection of data engineering and machine learning, collaborating closely with data scientists, software engineers, and product teams to turn models and agents into robust, production-ready systems.

This is a high-impact role suited for someone who thrives on ownership, scalability, and deploying real-world ML and AI solutions at scale.

Key Responsibilities

ML & AI System Development

• Design and build end-to-end ML pipelines, from data ingestion and feature engineering to model training, serving, and monitoring.
• Productionize AI agents, including LLM-backed workflows, multi-step tool use, and API orchestration.
• Collaborate with data scientists to bring experimental models into stable, high-performing production systems.
  
  

MLOps & Infrastructure

• Implement and maintain MLOps workflows (CI/CD for ML), tracking experiments, managing model versions, and automating retraining.
• Set up observability for models and agents, including tracing, logging, latency/error tracking, and fallback logic.
• Ensure infrastructure supports scalability, performance, and compliance requirements.
  
  

Technical Leadership & Collaboration

• Lead technical architecture for key AI and ML systems, ensuring maintainability and extensibility.
• Partner with product and platform teams to translate business needs into reliable and performant ML solutions.
• Mentor junior engineers and help elevate technical practices across the engineering org.

Minimum Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 8+ years of industry experience, with at least 4+ years in ML engineering or MLOps.
• Strong programming skills in Python and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
• Experience deploying AI agents in production, including retrieval-augmented generation (RAG), multi-step reasoning, and tool-use orchestration.
• Familiarity with LangChain, LlamaIndex, Haystack, or similar frameworks for agent development.
• Hands-on with LLMs (OpenAI, Anthropic, Cohere, etc.), embedding models, and vector store integration.
• Expertise in MLOps tools like MLflow, Airflow, SageMaker, or Kubeflow.
• Experience with cloud platforms (preferably AWS), Docker/Kubernetes, and large-scale data systems (e.g., Snowflake, Databricks).
• Excellent problem-solving and system design skills, including CI/CD, testing, and infrastructure-as-code.
  
  

Preferred Qualifications

• Experience with streaming pipelines, online inference, and model monitoring at scale.
• Exposure to agent memory, feedback loops, or AI workflow orchestration in customer-facing products.
• Prior contributions to open-source ML or LLM projects are a plus.
  
  

Why Join HG Insights?

• Work on high-impact AI products used by global enterprise customers.
• Lead the ML engineering strategy and shape our AI platform.
• Competitive compensation and benefits tailored for India-based employees.
• A collaborative and innovation-focused team environment.
  
  

Ready to build the future of AI infrastructure? Apply now to join HG Insights as a Staff Machine Learning Engineer in Pune.

Job Title: ML Full stack Engineer

 About Us

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the  British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery. 

WHY JOIN CAPCO? 

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

Job Description:

Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

About Us

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery. 

WHY JOIN CAPCO?

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

MAKE AN IMPACT

Job Details

Lead AI Engineer

Location: Pune (Hybrid)

Role Purpose Design and deliver AI/ML and Generative AI solutions that enhance client intelligence, automation, and decision-making across Private Banking platforms, leveraging data sourced from core banking systems (e.g. Avaloq), CRM, and digital channels.

Key Responsibilities

• Develop and deploy AI/ML and GenAI models (e.g. NLP, summarization, recommendation engines)
• Build scalable data pipelines and feature engineering workflows using structured and unstructured data
• Integrate AI capabilities into enterprise platforms via APIs and microservices.
• Work with data originating from core banking platforms (e.g. Avaloq) including client, portfolio, and transaction data.
• Enable AI-driven use cases such as client insights, next-best-action, and automation of RM workflows.
• Implement model monitoring, governance, and performance optimization aligned to enterprise standards.
• Collaborate with engineering and business teams to translate wealth use cases into scalable solutions.
• Experience working with wealth management data (clients, portfolios, transactions)
• Understanding of data flows from core banking systems such as Avaloq
• Awareness of AI governance, model risk, and regulatory considerations in banking

Skills & Experience

Primary:

• Python (mandatory) • ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn) • GenAI / LLM frameworks (LangChain, OpenAI, etc.) • AWS (S3, Lambda, SageMaker, Bedrock)

Secondary: • Data engineering (ETL pipelines, feature stores) • API development • Vector databases / embeddings

CSQ326R35

Mission

The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote.

The impact you will have:

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI research to solve customer problems
• Own production rollouts of consumer and internally facing GenAI applications
• Serve as a trusted technical advisor to customers across a variety of domains
• Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap 

What we look for:

• Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy
• Minimum of 5+ years of relevant experience as a Data Scientist preferably working in a consulting role
• Expertise in deploying production-grade GenAI applications, including evaluation and optimizations 
• Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.
• Experience building production-grade machine learning deployments on AWS, Azure, or GCP
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through AI
• [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets

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.

Sigmoid Analytics is a leading Data solutions company backed by Sequoia Capital. We offer best in- end-to-end data value chain spanning across Data Science, Data Engineering and Data Ops. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modelled around expertise and mutual respect with a team first mindset. You’ll work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire.

Offices: New York | Dallas | San Francisco | Lima | Bengaluru

The below role is for our Bengaluru office.

About the Role:

You will work on a broad range of cutting-edge data science and machine learning problems across a variety of industries. You will be engaging with clients to understand their business context. If you are passionate to work on complex unstructured business problems that can be solved using data science and machine learning we would like to talk to you.

Function:  Data Science and Analysis → Data Science / Machine Learning

Desired Skills & Competencies:

• Strong learning acumen
• Team Management
• High sense of ownership
• Ability to work in a fast-paced and deadline-driven environment
• Loves technology
• Highly skilled at Data Interpretation
• Problem solver
• Good exposure to machine learning concepts and algorithms
• Must be fluent with any one of Python, R, Java
• Strong in statistical & machine learning concepts
• Knowledge of Python Libraries - SciPy, NumPy, Pandas, I Python, Scikit-learn
• Knowledge of distributed big data processing (PySpark, Jupyter, Linux, AWS)

Responsibilities:

• Hypothesis testing, insights generation, root cause analysis, factor analysis
• Statistical model (predictive & prescriptive) development using various statistical & machine learning techniques/algorithms
• Test/train the model, Improve Model accuracy, Monitor model performance
• Data Extraction from EDW/Big Data Platform, Dataset Preparation (creation of base data, aggregation, transformation), performing EDA

Desired Experience & Education:

• 3 - 5 years of relevant Machine Learning experience.
• B. Tech from Tier-1 college / M.S or M. Tech is preferred in Computer Science, Information Technology, or similar degree.

Key Responsibilities

As a AI Engineer/Senior AI Engineer and a key member of our engineering team in India, you will be responsible for designing, building, and owning the end-to-end lifecycle and release of our most critical AI-powered features within a microservices architecture. This role requires a balance of advanced technical depth in AI/ML and a strong sense of product ownership, ensuring our solutions deliver tangible user value at scale.

1. End-to-End AI System Ownership

• Design & Development: Design, build, and deploy production-grade AI systems, ranging from traditional Machine Learning models to modern LLM-powered features.
• Architecture & Deployment: Lead AI projects from concept to execution, including designing robust microservice architectures, modeling, rigorous evaluation, and secure deployment on AWS.
• Data Expertise: Work extensively with unstructured data, chunking, embeddings, vector databases (e.g., Pinecone, pgvector), and address complex real-world data quality challenges.
• Production Standards: Own the release process end-to-end, applying high engineering standards to ensure scalability, operability, and maintainability, aligning with Enboarder’s microservices philosophy.

1. Generative AI & LLM Focus

• LLM Integration: Evaluate, integrate, and operationalize LLM-based tools and APIs, including but not limited to Anthropic Claude and AWS Bedrock, to enhance product capabilities.
• Advanced Techniques: Apply expertise in prompt engineering, Retrieval-Augmented Generation (RAG), and advanced evaluation metrics for generative outputs.
• Agentic Systems: Explore and implement agentic AI frameworks, workflow orchestration (e.g., Langgraph, CrewAI, StrandsAgents), or multi-agent systems to automate complex user journeys.

1. Collaboration, Leadership, and Strategy

• Product Collaboration: Collaborate closely with product, design, and other engineering teams to translate business requirements into high-impact AI capabilities that drive measurable user and business outcomes.
• Technical Leadership: Stay updated on the latest AI/ML advancements, contribute to the company’s long-term technical strategy, and evaluate new technologies for adoption.
• Mentorship: Actively mentor and guide junior AI engineers, fostering a culture of technical excellence, clean code practices, and continuous experimentation.

Required Skills & Experience

• Total Experience: 6+ years of professional experience in the IT/Technology industry, with a substantial focus on building and deploying production systems.
• AI/ML Experience: 4+ years of direct experience in building and deploying ML or LLM-based systems in production environments, ideally within a high-traffic SaaS environment.
• Programming Mastery: Strong proficiency in Python and modern AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
• Generative AI Tooling: Hands-on experience with LLM-based frameworks such as LangChain, AWS Bedrock, or comparable in-house/open-source solutions.
• Vector Databases: Practical experience working with vector databases (e.g., FAISS, Pinecone, PGVector) for scalable similarity search and RAG implementation.
• Cloud Architecture: Extensive experience deploying, monitoring, and operating applications on AWS, demonstrating deep awareness of scalability, security, and cost-optimization in a microservices environment.

Preferred Qualifications (Nice to Have)

• Familiarity with FastAPI and containerization technologies (Docker, Kubernetes).
• Experience with MLOps tools such as MLflow, Airflow, or similar workflow orchestration platforms.
• Ability to balance technical trade-offs between model performance, inference cost, and system complexity in AI deployments.
• Direct experience with Fine Tuning or Pre-training large language models (LLMs) on domain-specific data.

*NOTE:* Hybrid work with 3 days in office.

Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell® Fusion, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior. Auto insurers, automakers, commercial mobility companies, and the public sector use insights from CMT’s platform to power risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT measures and protects tens of millions of drivers across the world every day.

We are embarking on a transformative journey with DriveWell Atlas, a groundbreaking initiative to build a family of novel AIs on telematics data. As a Senior Machine Learning Engineer, Foundation Models on the DriveWell Atlas team, you will help design and develop next-generation AI models that advance CMT’s capabilities in risk assessment, driver engagement, crash detection, and claims processing. This role offers the opportunity to work at the intersection of cutting-edge AI research and real-world telematics applications.

CMT is looking for a collaborative, customer-committed, and creative Senior Machine Learning Engineer, Foundation Models who wants to join us in making roads safer by making drivers better!

Responsibilities:

• Contribute to the design, training, and deployment of novel AI models for telematics applications
• Develop algorithms that model physical phenomena associated with vehicle and human movement
• Explore and implement advanced self-supervised learning methods tailored to multi-modal telematics sensor data
• Fine-tune upstream pretrained models or leverage their embeddings to solve downstream telematics tasks such as crash detection, driver risk scoring, and claims processing
• Build models robust to noise, missing data, and diverse real-world operating conditions
• Collaborate with engineers and product teams to integrate AI models into production systems
• Develop scalable training and inference pipelines using frameworks such as Ray, PyTorch DDP, or Horovod
• Optimize models for efficient deployment across cloud and edge/mobile environments
• Stay current with AI/ML research, applying new techniques to real-world telematics challenges
• Document and present findings, contributing to CMT’s AI/ML knowledge base
• Support junior team members through code reviews, collaboration, and knowledge sharing
• Complete any additional tasks as they arise

Qualifications:

• Bachelor’s degree or equivalent years of experience and/or certification in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or a related field
• 4+ years of relevant experience in AI/ML (post-degree)
• Strong hands-on experience developing and training deep learning models, preferably transformers for time-series or multimodal data
• Familiarity with self-supervised learning and representation learning on noisy sensor data
• Proficiency in Python and data science libraries (e.g., Pandas, NumPy, scikit-learn)
• Strong experience with PyTorch (preferred) or TensorFlow for deep learning
• Practical experience with distributed training methods and efficient model training
• Experience with data processing pipelines and ML infrastructure (e.g., Spark, Airflow, Docker, AWS/GCP/Azure)
• Excellent problem-solving skills and ability to translate complex business problems into AI solutions
• Strong communication skills to effectively present technical concepts to diverse audiences
• Product-focused mindset with a track record of delivering impactful ML solutions

Nice to Haves:

• Master’s or PhD preferred
• Experience with model interpretability and explainability techniques (XAI)
• Familiarity with ethical AI principles, bias detection, and mitigation strategies
• Exposure to MLOps practices for managing the ML lifecycle
• Publications or open-source contributions in AI/ML

Compensation and Benefits:

• Fair and competitive salary based on skills and experience
• Equity may be awarded in the form of Restricted Stock Units (RSUs)
• Medical Benefits (Health insurance, Personal accident insurance, Group term life insurance), gratuity, parental leave, sick leave and public holidays
• Employees are eligible for flexible allowances which includes Leave Travel Assistance, Telephone/Mobile Expenses, Professional development expenses, Meals Coupon, Vehicle Reimbursement
• Flexible scheduling and work from home policy depending on role and responsibilities

Additional Perks:

• Feel great working to improve road safety around the world!
• Join one of our many employee resource groups including Black, AAPI, LGBTQIA+, Women, Book Club and Health & Wellness
• Extensive education and employee assistance programs
• CMT will do all that is possible to support our employees and create a positive and inclusive work environment for all!

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are seeking a highly skilled Senior Machine Learning SDET to ensure the quality, reliability, and safety of our ML-powered healthcare products.

Job Description:

As a Senior Machine Learning SDET, you will be responsible for designing, implementing, and maintaining robust testing frameworks and quality strategies for machine learning systems across their entire lifecycle. You will collaborate closely with data scientists, ML engineers, software engineers, and product managers to validate models, data pipelines, and ML-driven services in production environments. Your work will help ensure that our ML solutions are accurate, performant, secure, and compliant with healthcare standards, ultimately improving patient care and operational efficiency.

Key Responsibilities:

• Test Strategy for ML Systems: Define and own the end-to-end test strategy for ML models, data pipelines, and services, including functional, performance, regression, and reliability testing.
• ML Model Validation: Design automated tests to validate model behavior (e.g., accuracy, drift detection, bias, stability, and robustness) across training, staging, and production environments.
• Data Quality & Pipeline Testing: Build automated tests and monitoring for data pipelines to ensure schema integrity, data completeness, data freshness, and correctness for training and inference.
• Test Automation Frameworks: Develop and maintain scalable test automation frameworks and tools for APIs, microservices, and ML workflows, integrating them into CI/CD pipelines.
• Performance & Scalability: Design and execute load and performance tests for ML inference services and batch jobs, focusing on latency, throughput, and resource utilization.
• Monitoring & Observability: Collaborate with ML and platform engineers to implement monitoring, logging, and alerting for ML systems, including model performance and data drift.
• Shift-Left Quality: Embed quality practices early in the ML development lifecycle, including testable model design, test data generation, and automated validation in CI.
• Tooling & Infrastructure: Leverage and extend existing tools (e.g., Python test frameworks, cloud services, containerization, big data tools) to support robust ML testing.
• Collaboration: Work closely with cross-functional teams to understand business and regulatory requirements and translate them into verifiable test scenarios and acceptance criteria.
• Mentorship: Provide guidance and mentorship to engineers on best practices for testing ML and data-intensive systems, fostering a culture of quality and continuous improvement.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field; Ph.D. is a plus.
• Experience: Minimum of 5 years of experience in software test engineering or SDET roles, with at least 3 years focused on testing machine learning, data, or distributed backend systems in production.

Technical Skills:

• Strong proficiency in Python and testing frameworks such as pytest, unittest, or similar, with experience testing ML code and data pipelines.
• Hands-on experience with ML libraries and ecosystems (e.g., TensorFlow, PyTorch, scikit-learn) sufficient to design model validation and testing strategies.
• Hands-on experience with generative AI concepts (LLMs, RAG, vector databases, prompt engineering, and transfer learning) and their testing implications is a strong plus.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes) for deploying and testing ML services.
• Strong knowledge of data structures, algorithms, and software engineering best practices, including code reviews, version control, and CI/CD.
• Experience building and integrating automated tests for REST/gRPC APIs and microservices, including contract and integration testing.
• Experience with performance and load testing tools and techniques for backend and ML inference services.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, ELK, or cloud-native equivalents) for tracking model and system health.

Domain Knowledge:

• Understanding of healthcare data standards (e.g., HL7, FHIR) and regulations (e.g., HIPAA) is a plus.
• Experience testing systems in regulated, high-compliance, or safety-critical environments is highly desirable.

Soft Skills:

• Excellent problem-solving and analytical skills, with a strong focus on edge cases, failure modes, and risk assessment.
• Strong communication and collaboration abilities, with comfort working across ML, data, product, and platform teams.
• Ability to work independently and as part of a team in a fast-paced environment, balancing quality, speed, and pragmatism.

What We Offer:

• Competitive salary and benefits package.
• Opportunity to work on impactful ML-driven healthcare products that improve patient outcomes and operational efficiency.
• Collaborative and innovative work environment.
• Professional development and growth opportunities in ML quality engineering and SDET leadership.
• Flexible work arrangements, including hybrid and remote options.

Join us in ensuring that cutting-edge AI and machine learning solutions are not only powerful but also safe, reliable, and trustworthy in healthcare. We look forward to hearing from you.