Advisor - ML Engineering & Operations
Eli Lilly and Company9 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Own end-to-end ML architecture, feature engineering, and pipeline design for key commercial analytics initiatives.
- Design and review ML architecture, establish reusable engineering patterns, and make complex technical decisions.
- Own CI/CD orchestration, deployment, production monitoring, automation, and testing across multiple projects.
- Optimize model hyperparameters and evaluate production model performance, robustness, and explainability.
- Design and build production agentic AI systems with multi-step reasoning, tool use, and workflow orchestration.
- Own LLMOps practices including prompt versioning, evaluation pipelines, cost and latency monitoring, and production guardrails.
- Architect retrieval-augmented generation systems and integrate vector databases for production-scale semantic search and retrieval.
- Define technical contracts, APIs, integration boundaries, and shared SLAs with the Agentic AI engineering team.
- Establish human-in-the-loop boundaries with Data Scientists and business stakeholders.
- Provide informal technical oversight to two or three junior engineers through design and code reviews and technical problem solving.
- Coordinate with Data Scientists, software engineers, infrastructure teams, and other stakeholders on ML and agentic pipelines.
Requirements
- 13+ years of demonstrated experience building production ML/AI systems, including versioning, lineage, monitoring, deployment, optimization, scalability, and automated pipelines.
- Substantial recent experience developing generative AI and agentic systems, including architectural design and implementation of end-to-end solutions.
- Strong knowledge of scikit-learn, PyTorch, TensorFlow, Keras, or equivalent ML frameworks, with the ability to productionize Data Scientist models.
- Strong Python and PySpark skills; working knowledge of R is preferred.
- Proficiency with AWS services such as SageMaker, Lambda, and other AWS or serverless services.
- Strong Docker, Kubernetes, and GitHub Actions experience.
- Extensive hands-on experience with LangGraph or a comparable agentic framework.
- Experience with MLflow, Kedro, and Prefect or similar MLOps frameworks.
- Production experience with LLM applications, prompt engineering, retrieval-augmented generation, evaluation, monitoring, and guardrails.
- Experience with Pinecone or a similar vector database at production scale.
- Experience defining technical contracts, APIs, or integration boundaries with another engineering team.
- Experience working in a Scrum or Agile environment.
- Excellent verbal and written communication skills for advocating technical solutions to technical and business audiences.
- Master’s or PhD in Computer Science, Computer Applications, or a related technical field, or equivalent specialization or certifications in ML/AI Engineering, with deep SDLC understanding.
Tech Stack
Categories
About Eli Lilly and Company
Eli Lilly and Company researches, develops, manufactures, and markets prescription medicines and biologics for patients, sold through healthcare providers, pharmacies, and payers. Core therapeutic areas include diabetes, oncology, immunology, neuroscience, and obesity. Founded in 1876 and headquartered in Indianapolis, it is a public company (NYSE: LLY) with global operations and is building a new advanced manufacturing site for gene therapy and other modalities in Lebanon, Indiana.