Advisor - ML Engineering & Operations

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work, but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease. We're looking for people who are determined to make life better for people around the world.

The Lilly Bengaluru Business Insights & Analytics team was started in 2017 with the objective of using innovative data mining and analytics to support business decisions to Marketing functions in the US and ex-US affiliates (focused on in-line and pre-launch brands). This team has rapidly grown and currently comprises of more than 100 staff members, with varied backgrounds and skills across data management, data sciences, analytical techniques, pharmaceutical commercial operations, and business insights. The team provides analytics outcomes for driving decision making across Lilly's Marketing, Sales, Medical Affairs, and a range of other functions.

To support the marketing teams in their decision-making, a data and analytics team has been set up simultaneously in Indianapolis (HQ) and Bengaluru (Lilly Bengaluru). This team is responsible for setting up the data warehouses necessary to handle large volumes of digital streaming data, create meaningful analyses using that data, and deliver recommendations to leadership.

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 Lilly's 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 Lilly's 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 you'd 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 else's 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:

Master's 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

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation ) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

#WeAreLilly