Advisor - ML Engineering & Operations

Job Summary

As part of the Lilly Bengaluru team, we have an exciting opportunity for a Senior ML/AI Engineer who will own complex ML architecture and pipeline decisions across key initiatives while also leading technical integration with Lillys Agentic AI engineering team as our programs adopt agentic AI capabilities. This is a hands-on, individual contributor role that calls for genuine depth on both sides: production-grade ML engineering and agentic AI development.

Core Responsibilities

• Own end-to-end ML architecture, feature engineering, and pipeline design decisions for key commercial analytics initiatives
• Design and review ML architectural decisions with stakeholders, setting patterns that other engineers build on
• Own CI/CD pipeline orchestration, deployment (Docker/Kubernetes/Prefect), and production monitoring across multiple projects
• Apply software engineering rigor and best practices to ML systems, including CI/CD, automation, and testing
• Optimize model hyperparameters and evaluate model performance, robustness, and explainability across production ML systems
• Design and build production agentic AI systems using frameworks such as LangGraph - multi-step reasoning, tool use, and orchestration across complex workflows
• Own the LLMOps practice for initiatives you lead: prompt versioning, evaluation pipelines, cost/latency monitoring, and guardrails for production LLM applications (Claude or similar)
• Architect retrieval-augmented generation systems and integrate vector databases (e.g., Pinecone) for semantic search and retrieval at production scale
• Serve as the primary technical point of contact with Lillys Agentic AI engineering team, defining technical contracts, APIs, and shared SLAs as programs adopt agentic capabilities
• Set and document human-in-the-loop boundaries in partnership with Data Science and business stakeholders
• Provide informal technical oversight for 2-3 more junior engineers - reviewing designs and code, and unblocking hard technical problems, without formal people-management responsibility
• Coordinate with diverse stakeholders such as Data Scientists, software engineers, and infrastructure teams to design the most optimal ML and agentic pipelines

Required

• 13+ years of demonstrated expertise building ML/AI systems in production - including model versioning, data/model lineage, monitoring, deployment, optimization, scalability, and automated pipelines - with substantial recent depth in generative AI and agentic system development, not just brief exposure
• Knowledge of architectural design and implementation of end-to-end ML and agentic AI solutions
• Strong knowledge of core ML frameworks (scikit-learn, PyTorch, TensorFlow, Keras, or equivalent) and the ability to understand and extend the modeling work of Data Scientists into production-grade systems
• Strong knowledge of Python and PySpark for large-scale data processing; working knowledge of R is preferred (for Framebar pipeline hand-off from Data Science)
• Proficient in AWS components such as SageMaker, Lambda, and other AWS/serverless services (this role consumes cloud infrastructure rather than provisioning it directly)
• Strong knowledge of Docker, Kubernetes, and CI/CD tooling (GitHub Actions)
• Extensive hands-on experience with LangGraph or a comparable agentic framework, at a level where youd be setting patterns for others, not learning them
• Experience with MLOps frameworks such as MLflow, Kedro, and Prefect
• Production experience with LLM application development - prompt engineering, RAG architecture, and LLMOps practices (evaluation, cost/latency monitoring, guardrails)
• Experience with Pinecone or a similar vector database for semantic search/retrieval at production scale
• Experience defining technical contracts, APIs, or integration boundaries with another engineering team, not just consuming someone elses platform
• Experience developing in a Scrum/Agile environment
• Excellent verbal and written communication skills, with the ability to advocate technical solutions to Data Scientists, engineering teams, and business audiences

Education

• Masters or PhD in Computer Science, Computer Applications, or a related technical field, or equivalent specialization/certifications in ML/AI Engineering, with a deep understanding of SDLC

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