Advisor - ML Engineering & Operations

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