Advisor - ML Engineering & Operations
Eli Lilly and Company18 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Own ML architecture, feature engineering, pipeline design, and technical patterns for key commercial analytics initiatives.
- Design, orchestrate, deploy, and monitor production ML systems using Docker, Kubernetes, Prefect, and CI/CD automation.
- Optimize model hyperparameters and evaluate production model performance, robustness, and explainability.
- Build production agentic AI systems with multi-step reasoning, tool use, and workflow orchestration using LangGraph or comparable frameworks.
- Own LLMOps practices including prompt versioning, evaluation pipelines, cost and latency monitoring, and production guardrails.
- Architect retrieval-augmented generation systems and integrate vector databases such as Pinecone for semantic search and retrieval.
- Define technical contracts, APIs, integration boundaries, and shared SLAs with Lilly’s Agentic AI engineering team.
- Establish human-in-the-loop boundaries with Data Science and business stakeholders.
- Provide informal technical oversight to 2–3 junior engineers through design and code reviews and technical problem solving.
- Coordinate with Data Scientists, software engineers, infrastructure teams, and business stakeholders.
Requirements
- 13+ years of demonstrated experience building production ML/AI systems, including model versioning, lineage, monitoring, deployment, optimization, scalability, and automated pipelines.
- Expertise designing and implementing end-to-end ML and agentic AI solutions, with substantial recent generative AI and agentic system experience.
- Strong knowledge of scikit-learn, PyTorch, TensorFlow, Keras, or equivalent ML frameworks.
- Strong Python and PySpark skills; working knowledge of R is preferred.
- Proficiency with AWS services including SageMaker, Lambda, and other serverless services.
- Strong knowledge of Docker, Kubernetes, and GitHub Actions.
- 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, RAG architecture, evaluation, cost and latency monitoring, and guardrails.
- Experience with Pinecone or a similar vector database for production-scale semantic search and retrieval.
- Experience defining technical contracts, APIs, or integration boundaries with engineering teams.
- Experience working in a Scrum or Agile environment and communicating 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 a deep understanding of SDLC.
Tech Stack
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