Role 

We are looking for a Staff Machine Learning Engineer to join our Exposure Management & Security Operations team. This role is a hybrid position based in Bangalore, reporting to the Sr. Manager, Engineering Strategy, Planning & Analytics. You will join the team that built the world’s largest cloud security platform from the ground up, helping to scale a multitenant architecture that serves over 15 million users globally. Your vision and passion will be critical as we continue to innovate and enable organizations to harness the speed and agility of a cloud-first strategy.

What you’ll do (Role Expectations)

• Designing and deploying scalable, reliable, and efficient production-grade Gen AI/ML systems from data ingestion to monitoring
• Driving innovation by researching and evaluating emerging AI/ML frameworks, rapidly prototyping novel solutions, and championing full-scale implementation
• Implementing and maintaining robust MLOps practices, including logging, monitoring, and CI/CD pipelines for distributed ML systems
• Leading and mentoring junior engineers in system design best practices and promoting technical excellence
• Collaborating with cross-functional teams to translate complex business needs into technical solutions

Who You Are (Success Profile)

• You thrive in ambiguity and are comfortable building the path as you walk it, seeing dynamic environments as the raw material to build something meaningful.
• You act like an owner with a passion for the mission and a bias for action, navigating seamlessly between high-level strategy and hands-on execution.
• You are a problem-solver who seeks out challenges because you are energized by finding solutions that deliver the biggest impact.
• You are a learner with a growth mindset, actively seeking feedback to become a better partner and a stronger teammate.
• You are driven by innovation and have a deep curiosity for how things work, believing in the power of technology to accelerate transformation.

What We’re Looking for (Minimum Qualifications)

• At least 5 years of experience as a Machine Learning Engineer with a track record of shipping complex, scalable ML systems to production
• Proven experience building Gen AI/ML systems with LLMs, fine-tuning, Retrieval-Augmented Generation (RAG), and Agentic AI in production environments
• Experience designing and implementing distributed ML systems with deep knowledge of MLOps, including monitoring and logging
• Strong computer science foundation in data structures, algorithms, and system design with expertise in Python and SQL
• Excellent communication and interpersonal skills to partner effectively across global engineering teams

What Will Make You Stand Out (Preferred Qualifications)

• Expertise with cloud services such as AWS, GCP, or Azure and ML platforms like Kubeflow or SageMaker
• Proficiency in systems programming languages like Go or Rust with an understanding of distributed systems and networking fundamentals
• A record of research, publications, or patents in AI/ML

#LI-Hybrid #LI-VV3

Introduction:

Are you searching for an opportunity to play a key role in driving the dramatic growth of a highly successful software company? 

At Poppulo, we’re working on what’s next in communications and workplace technology. As a pioneer in this industry, we understand that meaningfully reaching every employee is hard. And so is managing office space in a hybrid world. And so is improving the customer and guest experience. We exist to make each of these things easier. We exist to bring harmony to our customers. 

And we do that at enterprise scale. Our omnichannel employee communications, customer communications, and workplace experience platform is trusted by over 6,000 organizations today, reaching more than 35M employees and delivering content to 500,000+ digital signs.

We know there’s no such thing as a “perfect" candidate - we’re all a work in progress and are growing new skills and capabilities all the time. We encourage you to apply for a position with Poppulo even if you don’t meet 100% of the requirements. We believe in fostering an environment where there is a diversity of perspectives, in hopes that we can all thrive. 

The Opportunity

We are looking for a Sr. Machine Learning (ML) Engineer with a strong sense of ownership. If you are an ML practitioner who thrives in an environment where innovation meets practical application to build disruptive products, this role is for you. 

At Poppulo, we intend to redefine boundaries and reshape Employee Communications and Digital Signage through the power of cutting-edge machine learning and AI. We are not just building products; we are disrupting the status-quo with transformative solutions that challenge conventional thinking, create competitive edge and create new market opportunities. 

This role is pivotal to the success of our AI team. It is the backbone of our ML initiatives. This role bridges the gap between research and production by leveraging cutting-edge AI technology into leading, world-class solutions.  

As a Sr. Full-Stack Machine Learning (ML) Engineer, you be the key driver for innovative problem solving by combining technical excellence with creative application of ML. Your expertise will be crucial in uncovering hidden opportunities within product ideas, including the design and deployment of agentic AI systems that can reason, plan, and act autonomously within real-world product constraints. Additionally, you will stay at the forefront of emerging technologies, continuously evaluating new AI/ML frameworks, tools, and best practices to help guide the evolution of our software solutions. 

Key Responsibilities

• Own end-to-end delivery of AI features across model, backend, APIs, UI integration, deployment, monitoring, and iteration in production
• Solve complex challenges with AI/ML: Design, develop new AI-powered products that deliver the product roadmap, including agentic AI solutions that orchestrate LLMs, tools, and workflows to solve multi-step problems autonomously.
• Implement ML lifecycle - from data engineering and model development to cloud-based deployment, integrations and operationalisation. Incl. MLOps 
• Productionise full-stack AI/ML solutions: Translate emerging techs like GenAI& agenticAI architectures into innovative, practical solutions that transform customer experiences. 
• Align with Product Strategy: Create proof of concepts at high cadence to demonstrate/validate potential solutions as per our product strategy. 
• Optimise Model and system performance: Fine-tune, optimise training and inference performances, including latency, cost, and reliability trade-offs in agent-based and LLM-driven systems.
• Wider collaboration: Partner with cross-functional teams to demonstrate and validate the impact of ML innovations before introducing them into the product ecosystem. 
• Research Savvy: Staying up-to-date with SOTA and industry trends in AI/ML, with a strong awareness of advances in agentic systems, autonomous workflows, and multi-agent architectures. 

Technical Skills / Competencies  

• Strong AI/ML background: Expertise in designing, building and deploying real-world ML applications (Computer Vision, Classification, etc.) 
• Strong foundation in GenAI: Deep understanding of generative models (LLMs, etc.), AI Agents, prompt engineering RAG, vector databases. 
• Technical Expertise: Proficiency in ML/GenAI frameworks, databases, shell scripts and programming languages, including frameworks and tools commonly used for building and orchestrating AI agents.
• MLOps expertise: Hands-on experience in setting up MLOps on AWS, Azure and GCP. 
• Full-stack expertise: Proficiency in data pipelines, distributed systems, APIs, web front-end, mobile apps, automated testing and cloud platforms (AWS, GCP, etc.). 
• Exceptional problem-solving skills: Ability to simplify and breakdown complex technical and business challenges to create innovative and practical solutions. 
• Team management: Experience in guiding teams to deliver high-impact solutions. 
• Continuous learning: Ability to learn quickly and apply new technologies to solve problems practically. 

Education & Experience

• Master’s or PhD in AI/ML or a related field
• At least 6+ years of experience building software or AI systems, including 1–3 years working with LLMs, generative AI, or AI agent architectures.

Why Us? 

• An excellent workplace culture 
• Competitive salary 
• Company performance-related bonus
• Medical insurance 
• Flexible working hours 
• Educational assistance 
• In-house soft skills training 

Who We Are

We are a values-driven organization that encourages our employees to bring their authentic selves to work every day and empowers everyone to make a tangible impact on our products, clients, and culture. We offer a dynamic environment with driven, fun, and flexible individuals who thrive on challenge and responsibility. This is an opportunity to contribute to our culture and join a company that’s on the move.

We live the Poppulo values each day, as they are key to everything we do.

• Bring Your Best Self
  We show up authentically, are self-aware and always strive to be better.
• See it. Own it. Solve it.
  We proactively innovate and solve for our customers and each other. We set an example with high standards for our work. We foster a culture of learning, acknowledging our successes and our failures.
• Together We’re Better
  We value and celebrate our diversity. We learn from others, respecting their expertise, and focus on building trust. That's what makes us a team.

Named a Great Place to Work in 2015, 2016, 2017, 2018, 2019, 2020, and 2021, we are a fast-growing global technology company, with offices in Ireland, the US, the UK and India.

Poppulo is an equal opportunity employer.

We are committed to protecting your privacy. For details on how we collect, use, and protect your personal information, please refer to our Job Applicant Privacy Policy.

Introduction:

Are you searching for an opportunity to play a key role in driving the dramatic growth of a highly successful software company? 

At Poppulo, we’re working on what’s next in communications and workplace technology. As a pioneer in this industry, we understand that meaningfully reaching every employee is hard. And so is managing office space in a hybrid world. And so is improving the customer and guest experience. We exist to make each of these things easier. We exist to bring harmony to our customers. 

And we do that at enterprise scale. Our omnichannel employee communications, customer communications, and workplace experience platform is trusted by over 6,000 organizations today, reaching more than 35M employees and delivering content to 500,000+ digital signs.

We know there’s no such thing as a “perfect" candidate - we’re all a work in progress and are growing new skills and capabilities all the time. We encourage you to apply for a position with Poppulo even if you don’t meet 100% of the requirements. We believe in fostering an environment where there is a diversity of perspectives, in hopes that we can all thrive. 

The Opportunity

We are looking for a Sr. Machine Learning (ML) Engineer with a strong sense of ownership. If you are an ML practitioner who thrives in an environment where innovation meets practical application to build disruptive products, this role is for you. 

At Poppulo, we intend to redefine boundaries and reshape Employee Communications and Digital Signage through the power of cutting-edge machine learning and AI. We are not just building products; we are disrupting the status-quo with transformative solutions that challenge conventional thinking, create competitive edge and create new market opportunities. 

This role is pivotal to the success of our AI team. It is the backbone of our ML initiatives. This role bridges the gap between research and production by leveraging cutting-edge AI technology into leading, world-class solutions.  

As a Sr. Full-Stack Machine Learning (ML) Engineer, you be the key driver for innovative problem solving by combining technical excellence with creative application of ML. Your expertise will be crucial in uncovering hidden opportunities within product ideas, including the design and deployment of agentic AI systems that can reason, plan, and act autonomously within real-world product constraints. Additionally, you will stay at the forefront of emerging technologies, continuously evaluating new AI/ML frameworks, tools, and best practices to help guide the evolution of our software solutions. 

Key Responsibilities

• Own end-to-end delivery of AI features across model, backend, APIs, UI integration, deployment, monitoring, and iteration in production
• Solve complex challenges with AI/ML: Design, develop new AI-powered products that deliver the product roadmap, including agentic AI solutions that orchestrate LLMs, tools, and workflows to solve multi-step problems autonomously.
• Implement ML lifecycle - from data engineering and model development to cloud-based deployment, integrations and operationalisation. Incl. MLOps 
• Productionise full-stack AI/ML solutions: Translate emerging techs like GenAI& agenticAI architectures into innovative, practical solutions that transform customer experiences. 
• Align with Product Strategy: Create proof of concepts at high cadence to demonstrate/validate potential solutions as per our product strategy. 
• Optimise Model and system performance: Fine-tune, optimise training and inference performances, including latency, cost, and reliability trade-offs in agent-based and LLM-driven systems.
• Wider collaboration: Partner with cross-functional teams to demonstrate and validate the impact of ML innovations before introducing them into the product ecosystem. 
• Research Savvy: Staying up-to-date with SOTA and industry trends in AI/ML, with a strong awareness of advances in agentic systems, autonomous workflows, and multi-agent architectures. 

Technical Skills / Competencies  

• Strong AI/ML background: Expertise in designing, building and deploying real-world ML applications (Computer Vision, Classification, etc.) 
• Strong foundation in GenAI: Deep understanding of generative models (LLMs, etc.), AI Agents, prompt engineering RAG, vector databases. 
• Technical Expertise: Proficiency in ML/GenAI frameworks, databases, shell scripts and programming languages, including frameworks and tools commonly used for building and orchestrating AI agents.
• MLOps expertise: Hands-on experience in setting up MLOps on AWS, Azure and GCP. 
• Full-stack expertise: Proficiency in data pipelines, distributed systems, APIs, web front-end, mobile apps, automated testing and cloud platforms (AWS, GCP, etc.). 
• Exceptional problem-solving skills: Ability to simplify and breakdown complex technical and business challenges to create innovative and practical solutions. 
• Team management: Experience in guiding teams to deliver high-impact solutions. 
• Continuous learning: Ability to learn quickly and apply new technologies to solve problems practically. 

Education & Experience

• Master’s or PhD in AI/ML or a related field
• At least 6+ years of experience building software or AI systems, including 1–3 years working with LLMs, generative AI, or AI agent architectures.

Why Us? 

• An excellent workplace culture 
• Competitive salary 
• Company performance-related bonus
• Medical insurance 
• Flexible working hours 
• Educational assistance 
• In-house soft skills training 

Who We Are

We are a values-driven organization that encourages our employees to bring their authentic selves to work every day and empowers everyone to make a tangible impact on our products, clients, and culture. We offer a dynamic environment with driven, fun, and flexible individuals who thrive on challenge and responsibility. This is an opportunity to contribute to our culture and join a company that’s on the move.

We live the Poppulo values each day, as they are key to everything we do.

• Bring Your Best Self
  We show up authentically, are self-aware and always strive to be better.
• See it. Own it. Solve it.
  We proactively innovate and solve for our customers and each other. We set an example with high standards for our work. We foster a culture of learning, acknowledging our successes and our failures.
• Together We’re Better
  We value and celebrate our diversity. We learn from others, respecting their expertise, and focus on building trust. That's what makes us a team.

Named a Great Place to Work in 2015, 2016, 2017, 2018, 2019, 2020, and 2021, we are a fast-growing global technology company, with offices in Ireland, the US, the UK and India.

Poppulo is an equal opportunity employer.

We are committed to protecting your privacy. For details on how we collect, use, and protect your personal information, please refer to our Job Applicant Privacy Policy.

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.

Now, more than ever, the Toast team is committed to our customers. We’re taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we’ll deliver on their needs for today while investing in experiences that will power their restaurant of the future. 

Bready* to make a change?

Toast is looking for a Staff Machine Learning Engineer to serve as a technical linchpin for our AI Platform team. At the P4 level, you aren't just deploying models; you are designing the fundamental infrastructure that enables dozens of teams to build, deploy, and monitor AI at scale. You will act as a force multiplier, mentoring senior engineers and setting the architectural standards for our MLOps lifecycle—from feature stores and automated retraining to high-performance inference at the edge.

About this Roll*:

• Architectural Leadership: Design and lead the evolution of a unified MLOps platform that supports diverse needs across Toast, ensuring high availability, scalability, and security of ML services.
• Engineering Excellence: Champion and institutionalize best practices for CI/CD for ML (MLOps), automated testing, and infrastructure-as-code (Terraform).
• Cross-Functional Synergy: Lead collaborative efforts across Data Engineering, DevOps, and Product teams to bridge the gap between model prototyping and production-grade reliability.
• Strategic Roadmapping: Partner with leadership and Product Managers to define the 1-2 year technical vision for AI infrastructure, prioritizing long-term stability over short-term fixes.
• Operational Ownership: Set the standard for observability and incident response for ML systems, driving root-cause analysis for complex system failures.
• Mentorship: Actively mentor P2 and P3 engineers, fostering a culture of technical rigor and continuous learning.

Do you have the right ingredients*?

• Education: Bachelor’s or Master’s degree in Computer Science, AI, or a related technical field.
• Experience: A minimum of 10-12+ years of professional software engineering experience, with at least 6-7 years specifically focused on productionizing and scaling ML systems at the enterprise level.
• Core Tech Stack: Expert-level proficiency in Python, Scala, or Java/Kotlin. Extensive experience with PySpark and high-performance computing.
• Generative AI & LLMOps: Proven track record of taking LLM applications from research to production, including experience with Vector Databases, LangChain/LangGraph, and A2A protocols.
• System Design: Superior ability to design distributed systems that handle millions of requests with sub-second latency.
• Experience with microservice based architecture, preferably with AWS tooling (SageMaker, DynamoDB, Athena, Glue, etc.)
• Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration, MLflow
• Profound knowledge of model deployment, orchestration (Apache airflow, Prefect), scaling, and managing CPU/GPU resources efficiently. 
• Exceptional problem-solving, analytical skills and the ability to tackle complex problems with a critical thinking approach. 
• Outstanding communication and interpersonal skills, coupled with a demonstrated ability to work collaboratively within a team environment.
• Foundational knowledge in statistical concepts (e.g. classification, regression, etc) and deep learning algorithms (e.g. CNN, RNN) is desirable

Bonus Ingredients (The "Staff" Edge)

• Data Strategy: Experience implementing enterprise-grade Feature Stores (e.g., Tecton, Feast) and real-time streaming frameworks like Apache Flink or Ray.
• Full-Stack Visibility: Ability to dive into the UI/UX layer (React) or deep into the kernel/networking layer to debug performance bottlenecks.

Open Source/Community: Contributions to relevant open-source projects (MLflow, Kubeflow, etc.) or a history of speaking at industry conferences.

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.

Job Title: Associate Software Engineer, AI
Location: Bangalore
Reports to: Sr. Manager, AI/ML

A quick snapshot…

As an Associate Software Engineer, you’ll be working on developing robust solutions across next-generation AI/GenAI based products and solutions. As a critical member of our team, you will play a pivotal role in enhancing our design, development, and continued enhancement of our features along with writing unit & integration tests to ensure features never break.

Why it’s a big deal…

This is one of the critical roles under the AI team within Engineering. You will possess deep technical expertise as a AI engineer and a strong commitment to participate in the entire AI development lifecycle, including data collection, preprocessing, model training, evaluation, and deployment. This role works on multiple technologies and databases to make sure the product development moves towards rapid development, rich controls, more scalable, and better security.

Are you the person we’re looking for?

Related Experience. You should have at least 6 Months of experience in the software industry as a AI Engineer and the technical requirements necessary to design, develop, test and implement solutions.

Technical Skills. You’re likely to have proficiency in AI development technologies such as Python, FastAPI, PyTest, Celery and other Python frameworks and familiarity with AI/ML, GenAI, and MLOps concepts.  You also possess good understanding with object-oriented programming, concurrency, design patterns, and REST APIs. Your familiarity with CI/CD tooling such as Terraform and GitHub Actions and frameworks like LangChain, LangGraph, and databases like SQL and NoSQL such as MongoDB, MSSQL, or Postgres. Understanding of Data Structure & Algorithm analysis and its usage in scalable software design.

Strong communication and interpersonal skills. You’re not just comfortable engaging in collaborative discussions, but initiating them, too. You are skilled at reading and adapting to different communication styles. When you speak you are clear and concise. Your strong listening skills foster connections with our clients and allow you to accurately collect the right information so you can resolve issues most expediently.

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

Here’s what will give you an edge…

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

Strong troubleshooting skills and logic based thinking is your forte. This is an absolute must. Your proven ability to analyze and apply logical thinking to determine the root cause of an issue is fundamental to success in this role.

Clear, deliberate, and collaborative communicator. You don’t just engage in collaborative discussions, you initiate them regularly. You are clear and concise when you speak and write -- and even creative in terms of ensuring your messages are received and understood. You leave no room for assumptions or misunderstanding. It’s what sets you apart from the rest.

Exposure to mature SDLC practices. Understanding of Agile methodologies. Experience with unit testing, code coverage, and security scan tools. Proficient in working with source control tools like GitHub/Bitbucket.

#LI-RT1

For over 20 years, Smartsheet has helped people and teams achieve–well, anything. As the Intelligent Work Management Platform, we are redefining the velocity of work by uniting people, data, and AI to move businesses forward. We don’t just automate tasks; we eliminate execution silos and turn strategic vision into measurable enterprise impact. We’re creating a space to think big and take action, because when challenge meets purpose and AI-powered execution meets human ingenuity, that’s magic at work. It’s what we show up for every day, helping organizations not just keep up with change, but thrive because of it.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Manager AI/ML Ops Engineering, who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Focus on team members and coaching them to play to their strengths, grow and deliver peak performance
• Lead and mentor a team of AI/ML Ops engineers and operations specialists.
• Define and govern solutions for AI/ML operations, ensuring scalability, cost-efficiency, and reliability
• Develop and maintain standardized AI/MLOps workflows, including CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines
• Delegate and harness the aggregate strength of your team.
• Focuses on individual and team needs to foster a positive culture consistent with Smartsheet values.
• Actively helps individuals and the overall team to set priorities and focus on delivery of commitments
• Review team's designs and provide feedback on deployment safety, resilience, scale, performance, and security
• Lead and facilitate cross-team interactions, communication, and dependencies
• Ensure all changes are fully tested before being deployed
• Ensure deployment plans are well-considered and include appropriate scalability and load tests
• Work with stakeholders to align AI projects with business strategies.
• Ensure all AI/ML Ops solutions adhere to regulatory compliance, security, and ethical guidelines
• Drive Engineering and Operational excellence initiatives
• Perform other duties as assigned
  
  

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Experience building teams through recruiting and retention
• Experience in Leading and Mentoring a team of ML engineers and operations specialists.
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
• Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
• CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
• Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
• Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable
• Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
• Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
• Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
• Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
• Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
• Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
• Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
• CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
• Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
• Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable
• Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
• Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
• Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
• Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
• Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
• Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

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.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Principal Data & AI/ML Ops Engineers, who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform. 

You Will:

• Data Architecture and Design: Designing and overseeing the architecture of scalable and reliable data platforms, including data pipelines, storage solutions, and processing systems
• Data Modelling and Management:Developing and implementing data models, ensuring data quality, and establishing data governance policies
• Data Pipeline Development: Building and optimising data pipelines for ingesting, processing, and transforming large datasets from various sources
• Performance Optimisation: Identifying and resolving performance bottlenecks in data pipelines and systems, ensuring efficient data retrieval and processing
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Data Security and Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining data platform systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design involving large Petabytes of data with Databricks Lakehouse
• Experience in modern AI/Data infrastructure patterns, Semantics layer Organizing data for AI agents (metadata, context)
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python, SQL, and potentially Java or Scala
• Exposure to Snowflake and Data pipeline frameworks like Airbyte/Airflow is preferable
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Driving engineering excellence initiatives
• Legally eligible to work in India on an ongoing basis

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 12–15+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

• Documentation and teaching: you make complex systems understandable; you uplevel teams. 

About the role:

As a Sr. Manager, Platform & Architecture you will lead the definition, governance & continuous evolution of the enterprise data architecture vision and roadmap, ensuring strong alignment with business strategy and digital transformation goals. This role is accountable for establishing modern, secure, and scalable data platform architectures that enable advanced analytics, AI/ML, generative AI, and self service data capabilities across the organization.

You will provide strategic leadership in embedding AI into the enterprise data platform, enabling use cases such as predictive analytics, intelligent automation, and generative AI, while ensuring responsible AI practices, model governance, data quality, and regulatory compliance.

The role requires deep expertise in data architecture, cloud data platforms, analytics ecosystems, and platform security, combined with strong people and stakeholder management skills. You will guide the evaluation and adoption of emerging technologies, oversee PoC initiatives, and drive the operationalization of AI/ML and GenAI solutions.

Working closely with data engineering, analytics, AI/ML, security, and business leaders, you will ensure the delivery of trusted, high performing, and future ready data and AI platforms that scale with organizational needs.

What you'll do:

Strategic Leadership & Architecture Vision

• Define and own the enterprise data architecture vision and roadmap aligned with business strategy.
• Establish architectural principles, standards, and best practices across data modeling, integration, metadata, and analytics
• Guide architectural decisions to support scalability, performance, security, and future growth.
• Evaluate and approve new data technologies, platforms, and tools

Team Management & Development

• Lead, mentor, and manage data modelers & architects, platform engineers and data engineers
• Set goals, conduct performance reviews, and support career growth and skills development
• Ensure effective workload planning, prioritization, and resource allocation
• Promote a culture of collaboration, documentation, and architectural discipline

Data Platform & Architecture Design

• Design and oversee end to end data architectures, including ingestion, storage, processing, analytics, and consumption layers
• Define scalable patterns for batch, near real time, and streaming data processing
• Establish standards for data platforms, ingestion frameworks, transformation and modeling approaches, and BI/visualization tools
• Ensure platform reliability, performance, scalability, and cost optimization

Platform Engineering, Security & Governance

• Architect platforms with security by design, including IAM, RBAC, data encryption, masking, and auditing
• Collaborate with security and compliance teams to meet regulatory and data privacy requirements
• Define and enforce data governance standards, including metadata management, lineage, data quality, and observability
• Support platform monitoring, availability, and operational excellence

Advanced Analytics & Innovation

• Enable advanced analytics use cases, including forecasting, predictive modeling, and statistical analysis
• Support data science and analytics teams by designing architectures for ML pipelines, feature stores, and model deployment
• Lead Proof of Concept (PoC) initiatives to evaluate new data, analytics, and platform tools
• Assess emerging technologies for scalability, security, integration, and business value

Self-Service & Data Enablement

• Drive the self-service analytics strategy to empower analysts and business users
• Design curated, governed data layers and semantic models for trusted self-service access
• Reduce dependency on IT by enabling reusable data assets and standardized analytics frameworks
• Partner with stakeholders to align data products with business needs

Experience you'll need:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field
• 10 - 15+ years of experience in data engineering, analytics, or platform architecture roles
• Strong experience designing and implementing modern data architectures and cloud data platforms.
• Hands-on and architectural experience with:
  • Snowflake or similar cloud data warehouse/data lake experience
  • Tableau or similar BI tools
  • Fivetran or equivalent ingestion tools
  • dbt or similar data transformation frameworks
• Proficiency in SQL, Python, Spark, and data modeling techniques.
• Experience with cloud platforms such as AWS, Azure, or GCP

Preferred Qualifications:

• Experience with advanced analytics, forecasting, and predictive modeling.
• Knowledge of ML/AI platforms and MLOps concepts.
• Strong understanding of data security, governance, and compliance frameworks.
• Excellent communication and stakeholder management skills.
• Experience working in large-scale or enterprise Data & Analytics environments.

What You Will Be Doing

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

Gen AI Responsibilities

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

What We're Looking For

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

Nice to Have

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

Job Title: Staff Software Engineer, AI

Locations: Bangalore

Reports to: Sr. Manager, AI/ML.

A quick snapshot…

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

Why it’s a big deal…

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

Are you the person we’re looking for?

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

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

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

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

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

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

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

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

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

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

Here’s what will give you an edge…

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

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

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

Preferred Skill sets/Tools:

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

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

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

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

#LI-UB1

Solutions Architect

Role Overview 

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

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

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

Key Responsibilities 

Qualifications 

Must-Have 

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

Nice-to-Have 

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

Attributes 

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

Travel:

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

Senior Software Architect – Agentic AI & Cloud Platforms

Location: India (Bangalore / Remote Hybrid)
Company: Picarro

Role Overview

Picarro is seeking a highly accomplished Senior Software Architect to lead the design and evolution of our next-generation intelligent platforms. This role sits at the intersection of Agentic AI, cloud-native architecture, and real-time data systems, enabling cutting-edge scientific instrumentation and analytics products.

You will define architectural strategy, guide engineering teams, and build scalable, intelligent systems that integrate AI-driven decision-making with high-throughput data pipelines. This is a hands-on leadership role for someone who thrives in deep technical problem-solving while influencing organization-wide engineering direction.

What You’ll Do

• Architect Next-Gen Platforms:
  Design and scale distributed, cloud-native systems supporting real-time data ingestion, processing, and AI-driven workflows.
• Lead Agentic AI Integration:
  Drive architecture for agent-based AI systems (autonomous decisioning, orchestration, and reasoning frameworks) embedded into core products.
• Own Data & Streaming Architecture:
  Define and optimize event-driven systems leveraging Kafka and streaming pipelines for high-volume, low-latency data.
• Hands-on Technical Leadership:
  Provide deep technical guidance in Java and Python ecosystems, setting best practices for design, performance, and scalability.
• Cloud & Platform Strategy:
  Architect and evolve cloud infrastructure (AWS/Azure/GCP), focusing on resilience, observability, and cost efficiency.
• Cross-Functional Influence:
  Partner with product, hardware, and data science teams to translate complex requirements into scalable software architectures.
• Mentor & Elevate Engineering Talent:
  Coach senior engineers and drive architectural rigor, design reviews, and engineering excellence across teams.

What We’re Looking For

Core Technical Expertise

• 15+ years in software engineering with significant experience in architecting large-scale distributed systems
• Deep expertise in:
• Java (Spring Boot / microservices architecture)
• Python (data pipelines, AI/ML integration)
• Kafka / event streaming platforms
• Strong experience with cloud-native systems (AWS, Azure, or GCP)
• Proven track record building high-throughput, low-latency systems

Agentic AI & Modern Systems

• Hands-on experience or strong exposure to:
• Agentic AI frameworks / LLM-based systems
• Autonomous workflows, orchestration, and reasoning systems
• Familiarity with ML pipelines, model integration, or AI-driven architectures

Architecture & Leadership

• Demonstrated ability to define and drive end-to-end architecture strategy
• Strong system design skills across microservices, APIs, and distributed data systems
• Experience influencing and mentoring senior engineers and tech leads

Nice to Have

• Experience with scientific instrumentation, IoT, or hardware-software integrated systems
• Knowledge of real-time analytics, telemetry, or sensor data platforms
• Exposure to containerization and orchestration (Docker, Kubernetes)
• Background in data engineering or MLOps

• Strong capability to define solution-level data models and end-to-end data architecture spanning analyzers/instrument telemetry, on-prem systems, and cloud software platforms

Why This Role is Compelling

• Work on cutting-edge Agentic AI systems applied to real-world scientific and industrial challenges
• Influence platform architecture at a company level, not just a single product
• Operate at the intersection of hardware, software, and AI
• High visibility role with direct impact on Picarro’s next phase of innovation

About Picarro:

We are the world's leader in timely, trusted, and actionable data using enhanced optical spectroscopy. Our solutions are used in a wide variety of applications, including natural gas leak detection, ethylene oxide emissions monitoring, semiconductor fabrication, pharmaceutical, petrochemical, atmospheric science, air quality, greenhouse gas measurements, food safety, hydrology, ecology, and more. Our software and hardware are designed and manufactured in Santa Clara, California and are used in over 90 countries worldwide based on over 65 patents related to cavity ring-down spectroscopy (CRDS) technology and are unparalleled in their precision, ease of use, and reliability.

At Picarro, we are committed to fostering a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, sex, color, religion, national origin, protected veteran status, gender identity, social orientation, or disability. Posted positions are not open to third-party recruiters/agencies, and unsolicited resume submissions will be considered free referrals. 

At Picarro, we strive to ensure that all individuals, regardless of their abilities, have equal opportunities. If you are an individual with a disability and require reasonable accommodation to complete any part of the application process or are limited in the ability or unable to access or use this online application process and need an alternative method for applying, please contact Picarro, Inc. at disabilityassistance@picarro.com for assistance. 

Scopely is looking for a Senior AI Engineer to join our Gen AI team in Bangalore!

At Scopely, we care deeply about what we do and want to inspire play, every day - whether in our work environments alongside our talented colleagues, or through our deep connections with our communities of players. We are a global team of game lovers who are developing, publishing and innovating the mobile games industry, connecting millions of people around the world daily.

What You’ll Do 

For this role we are seeking an experienced and forward-thinking Senior AI Engineer with deep expertise in Artificial Intelligence to lead the design and development of intelligent systems powering analytics, automation, and immersive in-game experiences. This role involves prototyping, researching emerging AI methods, and delivering scalable solutions that integrate seamlessly into production pipelines and game technologies.

• Lead the strategic design, development, and deployment of advanced AI systems for Conversational Analytics, Knowledge Bases, QA Automation, and in-game intelligent experiences
• Architect and implement scalable AI solutions supporting real-time systems, automation pipelines, and player-facing features
• Drive innovation in:
• Designing structured protocols for managing AI model context across diverse applications
• Creating autonomous agents for decision-making and dynamic interaction
• Collaborate across disciplines to translate high-level goals into robust, AI-powered capabilities
• Mentor and guide engineers, promote best practices, and contribute to architectural decisions shaping long-term technical direction
• Present research, insights, and prototypes that influence product vision and accelerate the adoption of emerging AI technologies
• Lead efforts in AI infrastructure, including model deployment workflows, performance tuning, and system observability in production environments

What We’re Looking For

• 5+ years of professional experience in backend or full-stack development using Python, including hands-on AI experience with at least 2 years in a senior role.
• Hands-on experience using agentic AI in day-to-day development to build, integrate, and optimize workflows with sub-agents, skills, MCP’s, tool calling, etc.
• Strong hands-on experience in building scalable, high-performance APIs using frameworks like FastAPI, Flask, etc.
• Proven Track record of atleast 1 year of experience in cloud- native development in AWS /GCP or similar and containerization using Docker  
• Strong background in software engineering best practices like CI/CD, Git workflows, testing strategies, and production-grade code quality
• Solid understanding of distributed systems, microservices architectures, and data management with both SQL and NoSQL technologies
• A strong sense of ownership and product thinking — proactively seeking ways to create value and drive impact
• Excellent problem-solving and system design skills, with a methodical and scalable approach to complex AI challenges
• Strong communicator with the ability to explain technical concepts clearly and influence cross-team decisions

Bonus

• Experience with MLOps, reinforcement learning, computer vision, or game AI development
• Contributions to open-source AI projects or publications in AI-related domains

At Scopely, we create games for everyone - and want to ensure that the people behind our games reflect that! We are committed to creating a diverse, supportive work environment where everyone is treated with respect. We are committed to providing equal employment opportunities and welcome individuals from all backgrounds to join us & embrace the adventure!

What You’ll Do:

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

What are we looking for

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

We would love to see:

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

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

Role Overview

As a Senior Staff Software Engineer at JazzX AI, you will operate as a principal technical owner of the platform’s most critical architectural foundations. This is a highly hands-on, senior individual contributor role with company-wide technical influence.

You will lead the design and evolution of core platform systems, shape long-term technical strategy, and drive execution across multiple teams and domains. Your work will directly influence:

• The long-term architecture of JazzX’s AI-native SaaS + PaaS platform
• How AI capabilities are safely, reliably, and audibly operationalized at scale
• Platform reliability, performance, security, and developer experience
• Engineering standards, architectural principles, and technical decision-making across the organization

This role is for engineers who think in systems, operate with extreme ownership, and enjoy solving ambiguous, high-impact problems at platform scale.

What You Will Do

1. Platform & System Architecture

• Define and evolve the end-to-end architecture for core platform services that power JazzX’s AI-native enterprise system.
• Own architectural decisions across distributed systems, service orchestration, data flows, and platform extensibility.
• Ensure designs balance scalability, reliability, security, privacy, and operational excellence.
• Drive architectural reviews, RFCs, and long-term technical planning across teams.
• Lead the design of enterprise workflows involving multiple services, teams, and human/system interactions
• Use process modeling techniques (e.g., BPMN or equivalent) to clarify flows, ownership, failure paths, and execution semantics

2. Cloud-Native & Infrastructure Foundations

• Set technical direction for cloud-native architecture across compute, storage, networking, and deployment models.
• Champion cloud-first, cloud-agnostic patterns and infrastructure best practices.
• Guide containerization, orchestration, and Infrastructure-as-Code standards.
• Partner closely with SRE, DevOps, and Security teams to build resilient, observable, and cost-efficient systems.

3. Developer Experience & Platform Enablement

• Design and build foundational APIs, frameworks, and abstractions that enable application and AI engineers to move fast without compromising correctness or safety.
• Establish platform contracts, service boundaries, and internal standards for extensibility and reuse.
• Ensure platform components are modular, composable, and well-documented.
• Act as a force multiplier for engineering velocity across the organization.

4. Operational Excellence & Reliability

• Own production readiness for mission-critical platform components, including availability, latency, scalability, and fault isolation.
• Define and enforce SLAs, SLOs, and reliability standards.
• Drive best practices around observability, distributed tracing, alerting, and incident response.
• Continuously improve operational maturity as the platform scales.

5. Technical Leadership & Organizational Influence

• Serve as a senior technical leader influencing platform direction through execution, not hierarchy.
• Mentor Staff and Senior engineers, raising the bar for systems thinking, design quality, and technical rigor.
• Collaborate deeply with AI research, product, security, and design teams to translate complex requirements into robust platform capabilities.
• Act as a trusted technical advisor to senior engineering and product leadership.

What We’re Looking For

Required Qualifications

• 12+ years of experience designing, building, and operating large-scale, production-grade software systems.
• Proven track record of owning and evolving complex distributed systems or platform architectures.
• Deep expertise in cloud-native systems, service-oriented design, and distributed architectures.
• Strong hands-on experience with containerized environments (Docker, Kubernetes) and modern cloud platforms.
•  

Software Engineering Excellence

• Expert-level understanding of software design principles, clean code practices, and system-level trade-offs.
• Strong proficiency in Python, with deep experience in at least one of: Java -Primary , Go, or JavaScript.
• Demonstrated ownership across the full SDLC: design, implementation, testing, deployment, and operations.
• Ability to make sound architectural decisions under ambiguity and evolving requirements.

Platform, Data & Infrastructure Skills

• Experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
• Strong understanding of observability stacks, metrics, logging, and distributed tracing.
• Experience with event-driven systems, streaming platforms (e.g., Kafka), and both SQL and NoSQL databases.
• Familiarity with enterprise security, access control, and compliance considerations.
• Experience designing and implementing cross-system workflows (order processing, onboarding, approvals, etc.)
• Hands-on experience with BPMN-based or workflow-driven systems

AI / ML (Strong Plus)

• Experience building or operating platforms that support AI/ML or data-intensive workloads.
• Exposure to MLOps, AI service deployment, or AI-adjacent infrastructure.
• Experience integrating modern AI systems (LLMs, agents, retrieval, evaluation pipelines) into production platforms is a strong plus.

What Sets You Apart

• You think in platforms and systems, not just services.
• You lead through technical depth, execution, and influence, not authority.
• You are comfortable owning decisions with long-term architectural consequences.
• You care deeply about production quality, reliability, and maintainability.
• You thrive in environments where the platform is still being defined.

Staff Software Development Engineer  

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

What You Will Do 

Design and Architect Scalable Solutions: 

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

Collaborative Innovation: 

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

Driving Operational Excellence: 

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

Requirements 

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

Development Lifecycle: 

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

Technical Skills: 

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

AI/ML Experience: 

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

ChargePoint is looking for an AI Engineer who will  design, development, and operation of production‑grade Generative AI systems. You will architect and deliver LLM‑powered applications, including Copilot‑style experiences, AI agents, and automation at enterprise scale.

You will operate with high autonomy, own complex technical initiatives end‑to‑end, and influence GenAI architecture and standards across teams.

Reports To

Senior Manager-Information Security

What You Will Be Doing

As an AI Engineer, you will:

What You Will Bring to ChargePoint

• Proven ability to own ambiguous, complex problem spaces
• Experience influencing architecture and technical direction beyond your immediate team
• Strong system design and debugging skills for distributed AI systems
• Ability to balance speed, quality, and risk in production AI deployments
• Passion for building scalable, maintainable, and responsible AI platforms
• Strong communication skills with engineers, product managers, and leadership

Requirements

• 4 plus years of professional software engineering experience
• Strong development skills in Python (required); experience with Java, Go, or TypeScript is a plus
• Deep hands‑on experience with LLMs and Generative AI systems
• Strong experience with RAG, embeddings, vector search, and prompt engineering
• Experience building Copilot‑style or agent‑based AI systems
• Strong understanding of distributed systems, APIs, and microservices
• Experience deploying and operating systems using Docker/Kubernetes
• Experience with Azure, AWS, or GCP
• Familiarity with LLMOps / MLOps practices

Good to Have

• Experience with AI governance, compliance, or regulated enterprise environments
• Experience with vector databases and retrieval optimization
• Prior ownership of AI systems running in production at scale

Location

Remote location in India

Opportunity

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

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

Responsibilities

AI Model Development & Customization

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

Productionization & Deployment

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

Technical Leadership

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

Data Engineering & Management

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

Bias Mitigation, Evaluation & Governance

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

Cross-Functional Collaboration

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

Learning, Innovation & Thought Leadership

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

Qualifications

Education & Experience

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

Technical Skills

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

Professional Attributes

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

About GW RhythmX

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

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

Opportunity

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

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

Responsibilities

Healthcare-Focused AI System Design & Development

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

Infrastructure Optimization & MLOps

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

Monitoring, Observability & Reliability

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

Healthcare Compliance & Responsible AI

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

Cross-Functional Collaboration & Technical Leadership

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

Qualifications

Education & Experience

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

Technical Expertise

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

Professional Attributes

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

About GW RhythmX

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

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

Title: DevOps Engineer

Reporting to: Senior Director, Product Development
Location: Bengaluru (Bangalore)

Opportunity:

Responsibilities:

Infrastructure Development & Integration

• Design, implement, and manage cloud-native infrastructure (AWS, Azure, GCP) to support healthcare platforms, AI agents, and clinical applications.
• Build and maintain scalable CI/CD pipelines to enable rapid and reliable delivery of software, data pipelines, and AI/ML models.
• Design and manage Kubernetes (K8s) clusters for container orchestration, workload scaling, and high availability with integrated monitoring to ensure cluster health and performance
• Implement Kubernetes-native tools (Helm, Kustomize, ArgoCD) for deployment automation and environment management ensuring observability through monitoring dashboards and alerts
• Collaborate with Staff Engineers/Architects to align infrastructure with enterprise goals for scalability, reliability, and performance leveraging monitoring insights to inform architectural decisions.

System Optimization & Reliability

• Implement and maintain comprehensive monitoring, logging, and alerting mechanisms (Prometheus, Grafana, ELK, Datadog, AWS cloudwatch, AWS cloud trail) to ensure real-time visibility into system performance, resource utilization, and potential incidents.
• Implement monitoring, logging, and alerting mechanisms (Prometheus, Grafana, ELK, Datadog) to ensure system reliability and proactive incident response.
• Ensure data pipeline workflows (ETL/ELT, real-time streaming, batch processing) are observable, reliable, and auditable.
• Support observability and monitoring of GenAI pipelines, embeddings, vector databases, and agentic AI workflows.
• Proactively analyze monitoring data to identify bottlenecks, predict failures, and drive continuous improvement in system reliability.

Compliance & Security

• Support audit trails and compliance reporting through automated DevSecOps practices.
• Implement security controls for LLM-based applications, AI agents, and healthcare data pipelines, including prompt injection prevention, API rate limiting, and data governance.

Collaboration & Agile Practices

• Partner closely with software engineers, data engineers, AI/ML engineers, and product managers to deliver integrated, secure, and scalable solutions.
• Contribute to agile development processes including sprint planning, stand-ups, and retrospectives.
• Mentor junior engineers and share best practices in cloud-native infrastructure, CI/CD, Kubernetes, and automation.

Innovation & Technical Expertise

• Stay informed about emerging DevOps practices, cloud-native architectures, MLOps/LLMOps, and data engineering tools.
• Prototype and evaluate new frameworks and tools to enhance infrastructure for data pipelines, GenAI, and Agentic AI applications.
• Advocate for best practices in infrastructure design, focusing on modularity, maintainability, and scalability.

Requirements

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical discipline.
• 6+ years of experience in DevOps, Site Reliability Engineering, or related roles, with at least 3+ years building cloud-native infrastructure.
• Proven track record of managing production-grade Kubernetes clusters and cloud infrastructure in regulated environments.
• Experience supporting GenAI/LLM applications (e.g., OpenAI, Hugging Face, LangChain) and vector databases (e.g., Pinecone, Weaviate, FAISS).
• Hands-on experience supporting data pipeline products using ETL/ELT frameworks (Apache Airflow, dbt, Prefect) and streaming systems (Kafka, Spark, Flink).
• Experience deploying AI agents and orchestrating agent workflows in production environments.

Technical Proficiency

• Expertise in Kubernetes (K8s) for orchestration, scaling, and managing containerized applications.
• Strong proficiency in containerization (Docker) and Kubernetes ecosystem tools (Helm, ArgoCD, Istio/Linkerd for service mesh).
• Hands-on experience with Infrastructure as Code (Terraform, CloudFormation, or Pulumi).
• Proficiency with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, ArgoCD, Spinnaker).
• Familiarity with monitoring and observability tools (Prometheus, Grafana, ELK, Datadog, AWS cloud watch and AWS cloud trail), including setting up dashboards, alerts, and custom metrics for cloud-native and AI systems.
• Good to have: knowledge of healthcare data standards (FHIR, HL7) and secure deployment practices for AI/ML and data pipelines.

Professional Skills

• Strong problem-solving skills with a focus on reliability, scalability, and security.
• Excellent collaboration and communication skills across cross-functional teams.
• Proactive, detail-oriented, and committed to technical excellence in a fast-paced healthcare environment.

About Get Well:

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

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

About SAI Group:

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

About Netradyne:

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

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

Job Overview:

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

Key Responsibilities:

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

Mandatory Skills:

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

Qualifications and Education Requirements:

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

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

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!

Senior AI SDET (AI Engineering & Quality)

Location: Bengaluru, India – Hybrid

About Reltio

Reltio's AI-powered data unification and management platform—spanning entity resolution, multi-domain master data management (MDM), and data products—transforms siloed data from disparate sources into unified, trusted, and interoperable data. The Reltio Connected Data Platform™ delivers data where and when it's needed, empowering data and analytics leaders with unparalleled business responsiveness. Leading enterprise brands across industries rely on our award-winning, cloud-native MDM capabilities to improve efficiency, manage risk, and drive growth.

At Reltio, our values guide everything we do: Customer First, Better Together, Simplify and Share, Own It, and Always Better Than Yesterday. If you share these values, we invite you to join us.

Role Overview

We are looking for a powerhouse engineer who bridges AI development and automated validation. This is a builder-tester role — you will own the full lifecycle of AI features: from writing core Python logic for agentic workflows and tuning prompts, to architecting robust evaluation frameworks that ensure those systems are enterprise-ready.

Core Responsibilities

1. AI Development & Engineering

• Agentic Implementation: Design and implement autonomous agents using frameworks such as LangGraph, CrewAI, or AutoGen.
• Prompt Engineering: Own the prompt lifecycle — design, version, and tune system prompts to minimize hallucinations and maximize intent recognition.
• RAG Pipeline Development: Build and optimize Retrieval-Augmented Generation (RAG) components, including document ingestion, chunking strategies, and vector database indexing.
• Feature Prototyping: Rapidly prototype AI-driven features in Python to validate feasibility before full-scale integration.
• Data Curation: Build golden datasets and synthetic data generation scripts to train and evaluate models.

1. Advanced Validation & Quality Architecture

• Automated Evaluation (Eval-as-Code): Build automated pipelines to measure LLM performance across metrics such as faithfulness, relevancy, and toxicity.
• Non-Deterministic Testing: Develop strategies to test "fuzzy" outputs using LLM-assisted evaluation (using one LLM to grade another).
• Hybrid Framework Development: Design and maintain a dual-stack automation framework covering both backend/infrastructure and AI/ML validation.
• End-to-End Orchestration: Integrate AI tests into the MLOps pipeline so that every model deployment or prompt change triggers a full regression of the agent's reasoning capabilities.
• Mentorship & Standards: Serve as the team's subject matter expert, defining standards for writing inherently testable AI code.

Required Skills

AI & Data Science

• Python: Strong proficiency with Pandas, NumPy, LangChain, and LlamaIndex.
• LLMs: Hands-on experience with OpenAI, Anthropic, or open-source models (Mistral, Llama); solid understanding of temperature, top-p sampling, and tokenization.
• Vector Databases: Experience with Pinecone, Milvus, or Weaviate.
• AI Evals & Observability: Practical experience building LLM-judge evaluation pipelines.

Software Engineering & QA

• Production-Grade Code: Ability to write and review complex automation suites for distributed systems.
• Architecture: Familiarity with microservices, async processing (Kafka/RabbitMQ), and RESTful APIs.
• Performance Testing: Experience with JMeter or Locust to benchmark LLM inference latency and streaming responses.

What You Bring

• 5+ years of experience as an SDET, QA Automation Engineer, or AI Engineer.
• 2+ years of hands-on experience testing AI/ML products.
• Experience integrating AI tools and agents into quality lifecycle management.
• Proven ability to write and review production-quality code.
• Solid background in test case design, test planning, and issue lifecycle management in a continuous deployment environment.
• Experience working in agile environments and testing highly scalable SaaS applications.

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!

Senior AI SDET (AI Engineering & Quality)

Location: Bengaluru, India – Hybrid

About Reltio

Reltio's AI-powered data unification and management platform—spanning entity resolution, multi-domain master data management (MDM), and data products—transforms siloed data from disparate sources into unified, trusted, and interoperable data. The Reltio Connected Data Platform™ delivers data where and when it's needed, empowering data and analytics leaders with unparalleled business responsiveness. Leading enterprise brands across industries rely on our award-winning, cloud-native MDM capabilities to improve efficiency, manage risk, and drive growth.

At Reltio, our values guide everything we do: Customer First, Better Together, Simplify and Share, Own It, and Always Better Than Yesterday. If you share these values, we invite you to join us.

Role Overview

We are looking for a powerhouse engineer who bridges AI development and automated validation. This is a builder-tester role — you will own the full lifecycle of AI features: from writing core Python logic for agentic workflows and tuning prompts, to architecting robust evaluation frameworks that ensure those systems are enterprise-ready.

Core Responsibilities

1. AI Development & Engineering

• Agentic Implementation: Design and implement autonomous agents using frameworks such as LangGraph, CrewAI, or AutoGen.
• Prompt Engineering: Own the prompt lifecycle — design, version, and tune system prompts to minimize hallucinations and maximize intent recognition.
• RAG Pipeline Development: Build and optimize Retrieval-Augmented Generation (RAG) components, including document ingestion, chunking strategies, and vector database indexing.
• Feature Prototyping: Rapidly prototype AI-driven features in Python to validate feasibility before full-scale integration.
• Data Curation: Build golden datasets and synthetic data generation scripts to train and evaluate models.

1. Advanced Validation & Quality Architecture

• Automated Evaluation (Eval-as-Code): Build automated pipelines to measure LLM performance across metrics such as faithfulness, relevancy, and toxicity.
• Non-Deterministic Testing: Develop strategies to test "fuzzy" outputs using LLM-assisted evaluation (using one LLM to grade another).
• Hybrid Framework Development: Design and maintain a dual-stack automation framework covering both backend/infrastructure and AI/ML validation.
• End-to-End Orchestration: Integrate AI tests into the MLOps pipeline so that every model deployment or prompt change triggers a full regression of the agent's reasoning capabilities.
• Mentorship & Standards: Serve as the team's subject matter expert, defining standards for writing inherently testable AI code.

Required Skills

AI & Data Science

• Python: Strong proficiency with Pandas, NumPy, LangChain, and LlamaIndex.
• LLMs: Hands-on experience with OpenAI, Anthropic, or open-source models (Mistral, Llama); solid understanding of temperature, top-p sampling, and tokenization.
• Vector Databases: Experience with Pinecone, Milvus, or Weaviate.
• AI Evals & Observability: Practical experience building LLM-judge evaluation pipelines.

Software Engineering & QA

• Production-Grade Code: Ability to write and review complex automation suites for distributed systems.
• Architecture: Familiarity with microservices, async processing (Kafka/RabbitMQ), and RESTful APIs.
• Performance Testing: Experience with JMeter or Locust to benchmark LLM inference latency and streaming responses.

What You Bring

• 5+ years of experience as an SDET, QA Automation Engineer, or AI Engineer.
• 2+ years of hands-on experience testing AI/ML products.
• Experience integrating AI tools and agents into quality lifecycle management.
• Proven ability to write and review production-quality code.
• Solid background in test case design, test planning, and issue lifecycle management in a continuous deployment environment.
• Experience working in agile environments and testing highly scalable SaaS applications.

Role Overview

As a Senior Staff Software Engineer at JazzX AI, you will operate as a principal technical owner of the platform’s most critical architectural foundations. This is a highly hands-on, senior individual contributor role with company-wide technical influence.

You will lead the design and evolution of core platform systems, shape long-term technical strategy, and drive execution across multiple teams and domains. Your work will directly influence:

• The long-term architecture of JazzX’s AI-native SaaS + PaaS platform
• How AI capabilities are safely, reliably, and audibly operationalized at scale
• Platform reliability, performance, security, and developer experience
• Engineering standards, architectural principles, and technical decision-making across the organization

This role is for engineers who think in systems, operate with extreme ownership, and enjoy solving ambiguous, high-impact problems at platform scale.

What You Will Do

1. Platform & System Architecture

• Define and evolve the end-to-end architecture for core platform services that power JazzX’s AI-native enterprise system.
• Own architectural decisions across distributed systems, service orchestration, data flows, and platform extensibility.
• Ensure designs balance scalability, reliability, security, privacy, and operational excellence.
• Drive architectural reviews, RFCs, and long-term technical planning across teams.
• Lead the design of enterprise workflows involving multiple services, teams, and human/system interactions
• Use process modeling techniques (e.g., BPMN or equivalent) to clarify flows, ownership, failure paths, and execution semantics

2. Cloud-Native & Infrastructure Foundations

• Set technical direction for cloud-native architecture across compute, storage, networking, and deployment models.
• Champion cloud-first, cloud-agnostic patterns and infrastructure best practices.
• Guide containerization, orchestration, and Infrastructure-as-Code standards.
• Partner closely with SRE, DevOps, and Security teams to build resilient, observable, and cost-efficient systems.

3. Developer Experience & Platform Enablement

• Design and build foundational APIs, frameworks, and abstractions that enable application and AI engineers to move fast without compromising correctness or safety.
• Establish platform contracts, service boundaries, and internal standards for extensibility and reuse.
• Ensure platform components are modular, composable, and well-documented.
• Act as a force multiplier for engineering velocity across the organization.

4. Operational Excellence & Reliability

• Own production readiness for mission-critical platform components, including availability, latency, scalability, and fault isolation.
• Define and enforce SLAs, SLOs, and reliability standards.
• Drive best practices around observability, distributed tracing, alerting, and incident response.
• Continuously improve operational maturity as the platform scales.

5. Technical Leadership & Organizational Influence

• Serve as a senior technical leader influencing platform direction through execution, not hierarchy.
• Mentor Staff and Senior engineers, raising the bar for systems thinking, design quality, and technical rigor.
• Collaborate deeply with AI research, product, security, and design teams to translate complex requirements into robust platform capabilities.
• Act as a trusted technical advisor to senior engineering and product leadership.

What We’re Looking For

Required Qualifications

• 12+ years of experience designing, building, and operating large-scale, production-grade software systems.
• Proven track record of owning and evolving complex distributed systems or platform architectures.
• Deep expertise in cloud-native systems, service-oriented design, and distributed architectures.
• Strong hands-on experience with containerized environments (Docker, Kubernetes) and modern cloud platforms.
•  

Software Engineering Excellence

• Expert-level understanding of software design principles, clean code practices, and system-level trade-offs.
• Strong proficiency in Python, with deep experience in at least one of: Java -Primary , Go, or JavaScript.
• Demonstrated ownership across the full SDLC: design, implementation, testing, deployment, and operations.
• Ability to make sound architectural decisions under ambiguity and evolving requirements.

Platform, Data & Infrastructure Skills

• Experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
• Strong understanding of observability stacks, metrics, logging, and distributed tracing.
• Experience with event-driven systems, streaming platforms (e.g., Kafka), and both SQL and NoSQL databases.
• Familiarity with enterprise security, access control, and compliance considerations.
• Experience designing and implementing cross-system workflows (order processing, onboarding, approvals, etc.)
• Hands-on experience with BPMN-based or workflow-driven systems

AI / ML (Strong Plus)

• Experience building or operating platforms that support AI/ML or data-intensive workloads.
• Exposure to MLOps, AI service deployment, or AI-adjacent infrastructure.
• Experience integrating modern AI systems (LLMs, agents, retrieval, evaluation pipelines) into production platforms is a strong plus.

What Sets You Apart

• You think in platforms and systems, not just services.
• You lead through technical depth, execution, and influence, not authority.
• You are comfortable owning decisions with long-term architectural consequences.
• You care deeply about production quality, reliability, and maintainability.
• You thrive in environments where the platform is still being defined.

About the role:

We are looking for a Staff Software Engineer to help build and scale ABBYY’s AI platform. This role sits at the intersection of platform engineering, MLOps, and DevOps. You will own how AI services are built, deployed, observed, and evolved in production, with a strong focus on Kubernetes, cloud infrastructure, and ML lifecycle automation.

This is a hands-on technical leadership role. You will design systems, write production code, influence architecture, and mentor engineers.

Job Responsibilities: 

• Design and build scalable AI platform services using Python and microservice architectures
• Own DevOps and MLOps workflows including CI/CD, model deployment, versioning, and rollback
• Build and maintain Kubernetes-based platforms for AI workloads
• Work on data pipelines, dataset versioning, and auto-labeling workflows for model training
• Enable end-to-end ML lifecycle: data ingestion, training, evaluation, deployment, and monitoring
• Collaborate closely with ML researchers, product teams, and other platform engineers
• Drive best practices in software design, reliability, security, and observability
• Lead technical discussions, review designs, and mentor team members

Job Requirements:

• 10+ years of experience in backend or platform engineering
• Strong proficiency in Python (or similar backend languages)
• Solid experience building microservices and distributed systems
• Hands-on expertise with Kubernetes in production environments
• Strong understanding of DevOps and MLOps principles
• Experience with data management for ML (datasets, labeling, pipelines)
• Cloud experience with Azure or strong willingness to adopt Azure quickly
• Ability to think at system level and still deliver hands-on

Nice to have:

• Experience building internal AI/ML platforms
• Familiarity with model serving frameworks and inference optimization
• Exposure to auto-labeling, weak supervision, or human-in-the-loop systems
• Experience in enterprise or B2B SaaS environments

Here are some of our local benefits:

• Comprehensive medical, accidental, and life insurance 

• Weekly wellness sessions to support physical and mental well‑being 
• Generous paid time off policy 

#LI-MM1

Role Overview

As a Senior Staff Software Engineer at JazzX AI, you will operate as a principal technical owner of the platform’s most critical architectural foundations. This is a highly hands-on, senior individual contributor role with company-wide technical influence.

You will lead the design and evolution of core platform systems, shape long-term technical strategy, and drive execution across multiple teams and domains. Your work will directly influence:

• The long-term architecture of JazzX’s AI-native SaaS + PaaS platform
• How AI capabilities are safely, reliably, and audibly operationalized at scale
• Platform reliability, performance, security, and developer experience
• Engineering standards, architectural principles, and technical decision-making across the organization

This role is for engineers who think in systems, operate with extreme ownership, and enjoy solving ambiguous, high-impact problems at platform scale.

What You Will Do

1. Platform & System Architecture

• Define and evolve the end-to-end architecture for core platform services that power JazzX’s AI-native enterprise system.
• Own architectural decisions across distributed systems, service orchestration, data flows, and platform extensibility.
• Ensure designs balance scalability, reliability, security, privacy, and operational excellence.
• Drive architectural reviews, RFCs, and long-term technical planning across teams.
• Lead the design of enterprise workflows involving multiple services, teams, and human/system interactions
• Use process modeling techniques (e.g., BPMN or equivalent) to clarify flows, ownership, failure paths, and execution semantics

2. Cloud-Native & Infrastructure Foundations

• Set technical direction for cloud-native architecture across compute, storage, networking, and deployment models.
• Champion cloud-first, cloud-agnostic patterns and infrastructure best practices.
• Guide containerization, orchestration, and Infrastructure-as-Code standards.
• Partner closely with SRE, DevOps, and Security teams to build resilient, observable, and cost-efficient systems.

3. Developer Experience & Platform Enablement

• Design and build foundational APIs, frameworks, and abstractions that enable application and AI engineers to move fast without compromising correctness or safety.
• Establish platform contracts, service boundaries, and internal standards for extensibility and reuse.
• Ensure platform components are modular, composable, and well-documented.
• Act as a force multiplier for engineering velocity across the organization.

4. Operational Excellence & Reliability

• Own production readiness for mission-critical platform components, including availability, latency, scalability, and fault isolation.
• Define and enforce SLAs, SLOs, and reliability standards.
• Drive best practices around observability, distributed tracing, alerting, and incident response.
• Continuously improve operational maturity as the platform scales.

5. Technical Leadership & Organizational Influence

• Serve as a senior technical leader influencing platform direction through execution, not hierarchy.
• Mentor Staff and Senior engineers, raising the bar for systems thinking, design quality, and technical rigor.
• Collaborate deeply with AI research, product, security, and design teams to translate complex requirements into robust platform capabilities.
• Act as a trusted technical advisor to senior engineering and product leadership.

What We’re Looking For

Required Qualifications

• 12+ years of experience designing, building, and operating large-scale, production-grade software systems.
• Proven track record of owning and evolving complex distributed systems or platform architectures.
• Deep expertise in cloud-native systems, service-oriented design, and distributed architectures.
• Strong hands-on experience with containerized environments (Docker, Kubernetes) and modern cloud platforms.
•  

Software Engineering Excellence

• Expert-level understanding of software design principles, clean code practices, and system-level trade-offs.
• Strong proficiency in Python, with deep experience in at least one of: Java -Primary , Go, or JavaScript.
• Demonstrated ownership across the full SDLC: design, implementation, testing, deployment, and operations.
• Ability to make sound architectural decisions under ambiguity and evolving requirements.

Platform, Data & Infrastructure Skills

• Experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
• Strong understanding of observability stacks, metrics, logging, and distributed tracing.
• Experience with event-driven systems, streaming platforms (e.g., Kafka), and both SQL and NoSQL databases.
• Familiarity with enterprise security, access control, and compliance considerations.
• Experience designing and implementing cross-system workflows (order processing, onboarding, approvals, etc.)
• Hands-on experience with BPMN-based or workflow-driven systems

AI / ML (Strong Plus)

• Experience building or operating platforms that support AI/ML or data-intensive workloads.
• Exposure to MLOps, AI service deployment, or AI-adjacent infrastructure.
• Experience integrating modern AI systems (LLMs, agents, retrieval, evaluation pipelines) into production platforms is a strong plus.

What Sets You Apart

• You think in platforms and systems, not just services.
• You lead through technical depth, execution, and influence, not authority.
• You are comfortable owning decisions with long-term architectural consequences.
• You care deeply about production quality, reliability, and maintainability.
• You thrive in environments where the platform is still being defined.

About SAI Group

SAI Group is a private investment firm that has committed $1 billion to incubate and scale revolutionary AI-powered enterprise software application companies. Our portfolio, a testament to our success, comprises rapidly growing AI companies that collectively cater to over 2,000+ major global customers, approaching $750 million in annual revenue, and employing a global workforce of over 4,000 individuals.

SAI Group invests in new ventures based on breakthrough AI-based products that have the potential to disrupt existing enterprise software markets. SAI Group’s latest investment, JazzX, is a pioneering technology company which is building a platform that will not only shape the future of enterprise AI applications but also offer practical solutions to real-world challenges.

Senior Software Engineer, JazzX (New AI Venture at SAI Group) 

Role Overview:  

As a Senior Software Development Engineer, you'll lead the design, development, and management of critical services and components of an innovative enterprise AI platform to deliver transformative, AI-enabled work experiences for enterprise users. You'll mentor junior engineers, collaborate with cross-functional teams, and drive towards building a cutting-edge enterprise AI platform that remains state of the art.  

Design and Build: 

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

Collaboration: 

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

Operational Excellence: 

• Own the seamless integration of developed components into the platform, ensuring efficient operation and high availability.
• Implement robust monitoring and alerting strategies to proactively identify and troubleshoot potential issues.
• Champion DevOps practices streamlining the software development lifecycle and delivery process. 

Requirements 

Experience:  

• Minimum of 5 years in designing, building, and operating cloud services, with proficiency in container and orchestration frameworks such as Docker and Kubernetes. Experience with common software defined infrastructure (infrastructure-as-code) frameworks to deploy and manage cloud environments. AI/ML/Data experience is strongly preferred. 

 Development Lifecycle:  

• Comprehensive hands-on experience in the full-stack development lifecycle, including design, prototyping, building, testing, deployment, and operations of enterprise platforms and products, preferably with AI/ML/data components. 

 Technical Skills:  

• Deep knowledge of essential software development skills and experience in more than 1 of the following languages: Java, JavaScript, Python, Golang, or C++.
• AI/ML Experience (Strongly Preferred): Prior experience in building and managing ML/AI platforms and services, including familiarity with MLOps lifecycle, frameworks, and tools. Familiarity with modern GenAI/LLM services, AI agent frameworks like LangChain, LlamaIndex is highly desired.  
• Growth Mindset: A naturally curious attitude with a proactive approach to learning, experimenting, building, operating, and improving the development of a cutting-edge intelligent platform. Flexible, learn-it-all attitude, encouraging sharing and collaborative growth in an intense and fun work environment. 

Why Join Us 

At JazzX AI, you have the opportunity to become an integral part of a pioneering team that is pushing the envelope of AI capabilities to create an autonomous intelligence driven future. We champion bold innovation, continuous learning, and embrace the challenges and rewards of crafting something genuinely groundbreaking.

Your work will directly contribute to pioneering solutions that have the potential to transform industries and redefine how we interact with technology. As an early member of our team, your voice will be pivotal in steering the direction of our projects and culture, offering a unique chance to leave your mark on the future of AI.

We offer a competitive salary, equity options, and an attractive benefits package, including health, dental, and vision insurance, flexible working arrangements, and more.

We are an equal opportunity employer and celebrate diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Staff Software Development Engineer  

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

What You Will Do 

Design and Architect Scalable Solutions: 

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

Collaborative Innovation: 

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

Driving Operational Excellence: 

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

Requirements 

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

Development Lifecycle: 

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

Technical Skills: 

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

AI/ML Experience: 

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

Role Overview

As a Senior Staff Software Engineer at JazzX AI, you will operate as a principal technical owner of the platform’s most critical architectural foundations. This is a highly hands-on, senior individual contributor role with company-wide technical influence.

You will lead the design and evolution of core platform systems, shape long-term technical strategy, and drive execution across multiple teams and domains. Your work will directly influence:

• The long-term architecture of JazzX’s AI-native SaaS + PaaS platform
• How AI capabilities are safely, reliably, and audibly operationalized at scale
• Platform reliability, performance, security, and developer experience
• Engineering standards, architectural principles, and technical decision-making across the organization

This role is for engineers who think in systems, operate with extreme ownership, and enjoy solving ambiguous, high-impact problems at platform scale.

What You Will Do

1. Platform & System Architecture

• Define and evolve the end-to-end architecture for core platform services that power JazzX’s AI-native enterprise system.
• Own architectural decisions across distributed systems, service orchestration, data flows, and platform extensibility.
• Ensure designs balance scalability, reliability, security, privacy, and operational excellence.
• Drive architectural reviews, RFCs, and long-term technical planning across teams.
• Lead the design of enterprise workflows involving multiple services, teams, and human/system interactions
• Use process modeling techniques (e.g., BPMN or equivalent) to clarify flows, ownership, failure paths, and execution semantics

2. Cloud-Native & Infrastructure Foundations

• Set technical direction for cloud-native architecture across compute, storage, networking, and deployment models.
• Champion cloud-first, cloud-agnostic patterns and infrastructure best practices.
• Guide containerization, orchestration, and Infrastructure-as-Code standards.
• Partner closely with SRE, DevOps, and Security teams to build resilient, observable, and cost-efficient systems.

3. Developer Experience & Platform Enablement

• Design and build foundational APIs, frameworks, and abstractions that enable application and AI engineers to move fast without compromising correctness or safety.
• Establish platform contracts, service boundaries, and internal standards for extensibility and reuse.
• Ensure platform components are modular, composable, and well-documented.
• Act as a force multiplier for engineering velocity across the organization.

4. Operational Excellence & Reliability

• Own production readiness for mission-critical platform components, including availability, latency, scalability, and fault isolation.
• Define and enforce SLAs, SLOs, and reliability standards.
• Drive best practices around observability, distributed tracing, alerting, and incident response.
• Continuously improve operational maturity as the platform scales.

5. Technical Leadership & Organizational Influence

• Serve as a senior technical leader influencing platform direction through execution, not hierarchy.
• Mentor Staff and Senior engineers, raising the bar for systems thinking, design quality, and technical rigor.
• Collaborate deeply with AI research, product, security, and design teams to translate complex requirements into robust platform capabilities.
• Act as a trusted technical advisor to senior engineering and product leadership.

What We’re Looking For

Required Qualifications

• 12+ years of experience designing, building, and operating large-scale, production-grade software systems.
• Proven track record of owning and evolving complex distributed systems or platform architectures.
• Deep expertise in cloud-native systems, service-oriented design, and distributed architectures.
• Strong hands-on experience with containerized environments (Docker, Kubernetes) and modern cloud platforms.
•  

Software Engineering Excellence

• Expert-level understanding of software design principles, clean code practices, and system-level trade-offs.
• Strong proficiency in Python, with deep experience in at least one of: Java -Primary , Go, or JavaScript.
• Demonstrated ownership across the full SDLC: design, implementation, testing, deployment, and operations.
• Ability to make sound architectural decisions under ambiguity and evolving requirements.

Platform, Data & Infrastructure Skills

• Experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
• Strong understanding of observability stacks, metrics, logging, and distributed tracing.
• Experience with event-driven systems, streaming platforms (e.g., Kafka), and both SQL and NoSQL databases.
• Familiarity with enterprise security, access control, and compliance considerations.
• Experience designing and implementing cross-system workflows (order processing, onboarding, approvals, etc.)
• Hands-on experience with BPMN-based or workflow-driven systems

AI / ML (Strong Plus)

• Experience building or operating platforms that support AI/ML or data-intensive workloads.
• Exposure to MLOps, AI service deployment, or AI-adjacent infrastructure.
• Experience integrating modern AI systems (LLMs, agents, retrieval, evaluation pipelines) into production platforms is a strong plus.

What Sets You Apart

• You think in platforms and systems, not just services.
• You lead through technical depth, execution, and influence, not authority.
• You are comfortable owning decisions with long-term architectural consequences.
• You care deeply about production quality, reliability, and maintainability.
• You thrive in environments where the platform is still being defined.

Role Overview

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

You will:

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

Key Responsibilities

Solution Architecture & Implementation

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

Delivery Leadership

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

Collaboration & Enablement

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

Innovation & Thought Leadership

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

Qualifications

Must-Have

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

Nice-to-Have

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

Attributes We Value

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

Senior Staff Engineer

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

Key Responsibilities 

Technical Leadership & Architecture 

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

Project Execution & Cross-Functional Collaboration 

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

Mentorship & Team Development 

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

Innovation & AI Integration 

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

Operational Excellence 

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

 Qualifications 

Educational Background 

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

Experience & Technical Expertise 

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

AI/ML Experience: 

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

Leadership & Soft Skills 

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

Job Title: Senior AGI Engineer – GTM Solution Delivery 

Role Overview 

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

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

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

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

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

Key Responsibilities 

Qualifications 

Must-Have 

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

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

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

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

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

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

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

Nice-to-Have 

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

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

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

Attributes 

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

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

• Detail-Oriented: You care about the little things  

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

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

Travel 

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

About JazzX AI

JazzX AI delivers value across the full AI application lifecycle:

1. Next-Generation Enterprise AI Platform A production-grade AI platform that orchestrates foundation models, reasoning systems, agents, retrieval, evaluation, and guardrails—designed for reliability, safety, and scale.

1. AI Builder Studio (Low-Code / No-Code)

A modern Builder Studio enabling developers and non-developers to design, compose, and deploy AI-powered workflows using visual and declarative abstractions .

1. First-Party Enterprise Applications Customer-facing enterprise applications built on the same platform, ensuring dogfooding, rapid iteration, and real-world validation at scale. We are starting with residential lending—a domain defined by deep due diligence, complex policies, and expert judgment—and are building a horizontal AI platform that will extend across knowledge-intensive industries.

Role Overview

As a Senior Staff Software Engineer at JazzX AI, you will operate as a principal technical owner of the platformʼs most critical architectural foundations. This is a highly hands-on, senior individual contributor role with company-wide technical influence.

You will lead the design and evolution of core platform systems, shape long term technical strategy, and drive execution across multiple teams and domains. Your work will directly influence:

• The long-term architecture of JazzXʼs AI-native SaaS + PaaS platform
• How AI capabilities are safely, reliably, and audibly operationalized at scale
• Platform reliability, performance, security, and developer experience
• Engineering standards, architectural principles, and technical decision making across the organization

This role is for engineers who think in systems, operate with extreme ownership, and enjoy solving ambiguous, high-impact problems at platform scale.

What You Will Do

1. Platform & System Architecture

• Define and evolve the end-to-end architecture for core platform services that power JazzXʼs AI-native enterprise system.
• Own architectural decisions across distributed systems, service orchestration, data flows, and platform extensibility.
• Ensure designs balance scalability, reliability, security, privacy, and operational excellence .
• Drive architectural reviews, RFCs, and long-term technical planning across teams.
• Lead the design of enterprise workflows involving multiple services, teams, and human/system interactions .
• Use process modeling techniques (e.g., BPMN or equivalent) to clarify flows, ownership, failure paths, and execution semantics.

1. Cloud-Native & Infrastructure Foundations

• Set technical direction for cloud-native architecture across compute, storage, networking, and deployment models.
• Champion cloud-first, cloud-agnostic patterns and infrastructure best practices.
• Guide containerization, orchestration, and Infrastructure-as-Code standards.
• Partner closely with SRE, DevOps, and Security teams to build resilient, observable, and cost-efficient systems.

1. Developer Experience & Platform Enablement

• Design and build foundational APIs, frameworks, and abstractions that enable application and AI engineers to move fast without compromising correctness or safety.
• Establish platform contracts, service boundaries, and internal standards for extensibility and reuse.
• Ensure platform components are modular, composable, and well documented.
• Act as a force multiplier for engineering velocity across the organization.

1. Operational Excellence & Reliability

• Own production readiness for mission-critical platform components, including availability, latency, scalability, and fault isolation.
• Define and enforce SLAs, SLOs, and reliability standards.
• Drive best practices around observability, distributed tracing, alerting, and incident response.
• Continuously improve operational maturity as the platform scales.

1. Technical Leadership & Organizational Influence

• Serve as a senior technical leader influencing platform direction through execution, not hierarchy.
• Mentor Staff and Senior engineers, raising the bar for systems thinking, design quality, and technical rigor.
• Collaborate deeply with AI research, product, security, and design teams to translate complex requirements into robust platform capabilities.
• Act as a trusted technical advisor to senior engineering and product leadership.

What Weʼre Looking For

Required Qualifications

• 12+ years of experience designing, building, and operating large-scale, production-grade software systems.
• Proven track record of owning and evolving complex distributed systems or platform architectures.
• Deep expertise in cloud-native systems, service-oriented design, and distributed architectures.
• Strong hands-on experience with containerized environments Docker, Kubernetes) and modern cloud platforms.

Software Engineering Excellence

• Expert-level understanding of software design principles, clean code practices, and system-level trade-offs.
• Strong proficiency in Python, with deep experience in at least one of: Java -Primary , Go, or JavaScript.
• Demonstrated ownership across the full SDLC: design, implementation, testing, deployment, and operations.
• Ability to make sound architectural decisions under ambiguity and evolving requirements.

Platform, Data & Infrastructure Skills

• Experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
• Strong understanding of observability stacks, metrics, logging, and distributed tracing.
• Experience with event-driven systems, streaming platforms (e.g., Kafka), and both SQL and NoSQL databases.
• Familiarity with enterprise security, access control, and compliance considerations.
• Experience designing and implementing cross-system workflows (order processing, onboarding, approvals, etc.)
• Hands-on experience with BPMN-based or workflow-driven systems .

AI / ML (Strong Plus)

• Experience building or operating platforms that support AI/ML or data intensive workloads.
• Exposure to MLOps, AI service deployment, or AI-adjacent infrastructure.
• Experience integrating modern AI systems (LLMs, agents, retrieval, evaluation pipelines) into production platforms is a strong plus.

What Sets You Apart

• You think in platforms and systems, not just services.
• You lead through technical depth, execution, and influence, not authority.
• You are comfortable owning decisions with long-term architectural consequences.
• You care deeply about production quality, reliability, and maintainability.
• You thrive in environments where the platform is still being defined.

About SAI Group

SAI Group is a private investment firm that has committed $1 billion to incubate and scale revolutionary AI-powered enterprise software application companies. Our portfolio, a testament to our success, comprises rapidly growing AI companies that collectively cater to over 2,000+ major global customers, approaching $600 million in annual revenue, and employing a global workforce of over 4,000 individuals.

SAI Group invests in new ventures based on breakthrough AI-based products that have the potential to disrupt existing enterprise software markets. SAI Group’s latest investment, JazzX AI, is a pioneering technology company which is building a platform that will not only shape the future of enterprise AI applications but also offer practical solutions to real-world challenges.

Sr. Engineering Manager

Role Overview 

As an Engineering Manager at JazzX AI, you will lead, mentor, and grow high-performing engineering teams that develop scalable, AI-driven, enterprise platforms and products. This is a hands-on technical and people leadership role where you will contribute to both technical and organizational initiatives, enabling JazzX AI to execute its mission of delivering transformative AGI-based solutions that transform the future of work in enterprises 

Key Responsibilities 

Team Leadership 

• Lead, mentor, and coach a team of talented AI engineers to achieve their career goals and maximize their impact.
• Foster a culture of collaboration, inclusivity, and innovation.
• Establish and uphold engineering best practices and processes 

Technical Ownership 

• Drive the design, architecture, and delivery of AI-based enterprise cloud systems and platforms.
• Collaborate with AI researchers, engineers, product managers, designers, and other stakeholders to align technical efforts with business objectives.
• Ensure high availability, scalability, and performance of JazzX AI’s systems. 

Project Management 

• Plan, prioritize, and manage technical projects, ensuring timely delivery of high-quality platforms and products
• Use Agile methodologies to organize sprints, retrospectives, and daily stan-ups.
• Own the quality and timelines of team execution, outputs and outcomes.  

Cross-Functional Collaboration 

• Partner with research and other engineers, product management, and UX design to define and execute product roadmaps.
• Communicate effectively with stakeholders, translating technical goals into business outcomes. 

Recruitment and Team Growth 

• Attract and hire top engineering talent to grow the team. 
• Build and maintain a pipeline of future engineering leaders. 

 Required Qualifications 

Experience: 

• 12+ years of experience in software engineering with 4+ years in an engineering management role.
• Proven track record of leading teams to deliver scalable, production-grade enterprise software platforms and products.
• Experience with AI/ML platforms and distributed systems 

Technical Expertise: 

• Proficiency in modern programming languages (e.g., Python, Java, Go, or similar).
• Strong knowledge of system design, architecture, and cloud-native technologies (e.g., AWS, Azure, or GCP).
• Familiarity managing AI/ML pipelines, data engineering, and MLOps best practices. 

Soft Skills: 

• Exceptional leadership and team-building skills.
• Excellent communication and interpersonal skills with an ability to bridge technical and non-technical stakeholders.
• Adept at conflict resolution, feedback delivery, and fostering a positive team environment. 

Preferred Qualifications 

• Experience in startups or building enterprise platforms and products from the ground up.
• Familiarity with Agile/Scrum methodologies.
• Hands-on experience with trade-offs in scaling AI systems and infrastructure.
• Understanding of compliance and security considerations in AI/ML implementations.  

Job summary

DigiCert is redefining the Domain Name System as an AI-native trust layer — enabling secure, intelligent, and autonomous resolution for the next era of the Internet.

As a Product Manager, you will lead innovation at the intersection of AI, DNS, and digital trust. You will collaborate with engineering, and GTM teams to build cloud-native DNS services that use inference and automation to detect anomalies, optimize performance, and strengthen security. You will also leverage AI tools to move fast — turning ideas and insights into a clear, measurable roadmap.

What you will do

• Define and own the AI-driven DNS roadmap, aligned with DigiCert’s trust and cloud strategy.
• Translate complex AI/ML capabilities into customer-focused, market-ready features.
• Partner with engineering and MLOps teams to deliver secure, scalable, cloud-native services.
• Use AI-assisted tools to analyze feedback, synthesize insights, and iterate quickly.
• Define and track success metrics — adoption, latency, reliability, trust-signal accuracy.
• Drive GTM execution with marketing and sales — launches, messaging, pricing, enablement.
• Engage customers, partners, and standards groups to validate use cases and guide innovation.
• Foster a data-driven product culture using telemetry, dashboards, and A/B experimentation.
• Ship production-ready features in days, not weeks, and iterate continuously.

What you will have

• 4+ years of product-management experience in cloud, networking, or infrastructure SaaS.
• Experience working closely with engineering teams on AWS, Azure or GCP.
• Understanding of DNS, networking, or distributed-systems fundamentals.
• Familiarity with AI/ML systems — especially inference pipelines or MLOps workflows (desired).
• Proven ability to deliver 0→1 products and iterate rapidly in agile environments.
• Strong analytical and storytelling skills; fluent in data-driven decision-making.
• Leverage AI-assisted tools (e.g., Figma Make, Cursor, vibe coding, Copilot) to:
  • Rapidly prototype workflows and UI concepts
  • Validate ideas early with engineers and customers
  • Iterate on requirements before build
• Experience supporting or leading GTM activities (launches, collateral, pricing, adoption tracking).
• Self-starter with an ownership mindset and a passion for 0→1 building in fast-moving environments.

Nice to have

• Startup speed, enterprise scale.
• Builders, not operators.
• Security, trust, and automation by default.
• AI-accelerated, open-source driven, cloud-native by design.

Benefits

• Generous time off policies
• Top shelf benefits
• Education, wellness and lifestyle support

#LI-SD1

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion’s DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You’ll own pipelines, microservices, CI/CD, observability, and runtime reliability—working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact. 

Why this Role Matters 

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products. 

• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders. 

• Operationalize secure, compliant, and explainable LLM and agentic services at scale. 

• Convert Applied Sciences models into scalable, compliant, cost‑efficient production services. 

• Standardize how models are trained, validated, deployed, and monitored across Tekion products. 

• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference. 

What You’ll Do 

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts. 

• Integrate with the LLM Gateway/MCP, prompt/config versioning. 

• Build and orchestrate CI/CD pipelines. 

• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality. 

• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems. 

• Monitor, detect, and mitigate risks unique to LLMs and agentic systems. 

• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics. 

• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph. 

• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets. 

• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage. 

• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking. 

• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation. 

• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path. 

Desired Skills and Experience 

• 6+ years in ML engineering/MLOps or backend/platform engineering with production ML. 

• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores. 

• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing. 

• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.). 

• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns. 

• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices. 

• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering. 

• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection. 

• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots. 

• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience. 

• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability. 

Preferred Mindset 

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics. 
• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs. 
• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency
• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach. 

Senior Software Engineer  -  Senior Machine Learning Platform

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 5 - 7 years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

• Documentation and teaching: you make complex systems understandable; you uplevel teams. 

Why project44?  

At project44, we believe in better.  

We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.  

With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.  

Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.  

If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk. 

Description:    

project44 is looking for a Staff Data Scientist to join our engineering team. You will work in a fast-paced Agile environment designing, building, and implementing best-in-class integrations to accelerate how project44 connects to the world’s logistics networks.   

About the Role

• Staff Data Scientist to lead the development of next-generation AI/ML systems at Project44, spanning ETA, risk, anomaly detection, and supply chain intelligence. This role sits at the intersection of applied modeling, platform thinking, and production impact, with a mandate to both ship high-value ML capabilities and build reusable Data Science platform primitives that scale across teams.
• In addition, this role will drive the integration of Generative AI, LLMs, and agentic systems into core workflows—enabling reasoning-driven diagnostics, automation, and intelligent decision support across the supply chain.
• You will work closely with Data Science, ML Engineering, Data Engineering, Platform, and Product teams to deliver production-grade systems and establish a scalable, repeatable approach to AI development at P44.

What You’ll Do

• Drive High-Impact ML Systems Lead end-to-end development of models for ETA, risk, anomaly, and fraud—leveraging advanced techniques (embeddings, transformers, hybrid models).
• Build Data Science as a Platform Develop reusable ML infrastructure (features, experimentation, deployment, monitoring) to scale model development and reduce time-to-production.
• Lead GenAI & Agentic Systems Build LLM-powered solutions (RAG, diagnostics, automation, coding agents) and establish guardrails, evaluation, and explainability.
• Translate Business Problems into ML Solutions Convert customer workflows into well-defined ML problems and ensure measurable business impact.
• Drive Experimentation & Evaluation Establish strong offline/online evaluation frameworks tied to business outcomes.
• Collaborate Across Engineering Partner with MLE and DE to build scalable, reliable systems across the ML lifecycle.

What We’re Looking For

• Experience 8–10+ years in Data Science / Applied ML with a strong track record of building and deploying production-grade ML systems.
• Core Modeling & Technical Expertise Deep expertise across tree-based models, transformers, probabilistic modeling, and feature engineering, with a strong data-centric mindset.
• Data & Platform Fluency Proficient in SQL and Python, with hands-on experience in modern data platforms (Snowflake/Databricks), pipelines (Spark, Airflow), streaming systems (Kafka), and MLOps tooling.
• GenAI & LLM Capability Experience building RAG systems, working with embeddings and vector databases, and developing LLM-based applications and agentic workflows. Strong understanding of evaluation, guardrails, and safe deployment of GenAI systems.
• Systems & Engineering Mindset Familiarity with distributed systems, APIs, and deployment patterns, with the ability to write clean, production-quality code.
• Analytical Rigor & Diagnostics Strong ability to evaluate model performance, detect edge cases, run root cause analysis, and design robust monitoring and evaluation frameworks.
• Data Quality & Signal Awareness Experience handling messy, real-world data, including drift, bias, missingness, and anomalies, along with tools and techniques to detect and address these issues.
• Modeling Judgment & Trade-offs Ability to identify when to use ML versus heuristics and design pragmatic hybrid solutions. Strong understanding of architectural trade-offs across models and systems.
• Business Impact Orientation Clear understanding of how ML metrics translate to business outcomes, with the ability to balance trade-offs across accuracy, scalability, latency, and customer experience.
• Leadership & Communication Strong problem framing, stakeholder influence, and ability to communicate model behavior and decisions clearly to both technical and non-technical audiences. Proven track record of driving measurable business impact.

Preferred Skills

• Experience in logistics (Ocean, Truckload), supply chain, or high-volume operational systems
• Experience with geospatial data, routing, and tracking systems
• Experience with anomaly detection, fraud modeling, ETA prediction
• Experience building internal platforms or tools for DS teams

What Success Looks Like (6–12 Months)

• Deliver 2 high-impact ML capabilities with measurable customer value
• Establish standardized experimentation, monitoring, and RCA workflows
• Introduce GenAI/agentic capabilities that improve productivity and insight generation
• Reduce time-to-production and increase reuse across DS teams
• Drive measurable improvements in model performance and system reliability

In-office Commitment: This position requires a commitment to contribute to our collaborative culture by working in-office three days weekly. 

Diversity & Inclusion 

At project44, we're designing the future of how the world moves and is connected through trade and global supply chains. As we work to deliver a truly world-class product and experience, we are also intentionally building teams that reflect the unique communities we serve. We’re focused on creating a company where all team members can bring their authentic selves to work every day. 

We’re building a company that every one of us at project44 is proud to work for, and our journey of becoming a more diverse, equitable and inclusive organization, where all have a sense of belonging, is shaped through the actions of our leadership, global teams, and individual team members. We are resolute in our belief that each team member has an equal responsibility to mold and uphold our culture. 

project44 is an equal opportunity employer seeking to enrich our work environment by creating opportunities for individuals of all backgrounds and experiences to thrive. If you share our values and our passion for helping the way the world moves, we’d love to review your application! 

For any accommodation needed during the hiring process, please email   recruiting@project44.com. Even if you don’t meet 100% of the above job description you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role’s requirements. 

More about project44 

Since 2014, project44 has been transforming the way one of the largest, most important global industries does business. As transportation and logistics continue to evolve and customer expectations around delivery become more demanding, industry technology must rise to the occasion. In just a few short years, we have created a digital infrastructure that eliminates the inefficiencies caused by dated technology and manual processes. Our Advanced Visibility Platform is used by the world’s leading brands to track shipments, collaborate with supply chain partners, drive operational efficiencies, and create outstanding customer experiences.