MLOps Engineer, Scientific Platforms for Discovery

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 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Locations: San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN

Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.

Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation & Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities—serving both human scientists and autonomous AI agents.

Position Summary

We are seeking an Engineer - MLOps & Scientific Platforms - Data Foundry to operationalize Data Foundry’s scientific tools and analytical methods into actionable-prototypes. You will build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails that make our scientific discovery methods and tools reliable, scalable, and consumable, both by discovery scientists and by the Frontier AI group’s autonomous agents.

This role sits at the interface between Methods4Insight (which develops analytical methods) and Architecture4Insight (which provides the agile data infrastructure). Your job is to ensure every scientific tool Data Foundry produces are analytics-ready, well-monitored, and exposed through APIs with the response-time guarantees and error handling that both human users and AI agents require.

Responsibilities

MLOps & Model Lifecycle Management

• Build and maintain end-to-end ML deployment pipelines: experiment tracking, model versioning (MLflow, Weights & Biases), containerized model serving, and automated retraining triggers.
• Develop model registry infrastructure and feature engineering pipelines that enable computational scientists to access models.
• Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems (LLMOps) to ensure system reliability and performance at scale.
• Build dashboards and metrics tracking for pipeline execution, API latency, token usage, model prediction quality, and system health
• Establish structured logging and tracing infrastructure for debugging and performance optimization across scientific data systems

Scientific Tool Agile Deployment

• Deploy predictive and analytical methods from Methods4Insight (e.g. cheminformatics, structural biology, bioinformatics, reaction informatics) with versioning, structured error handling, and response-time guarantees that enable insight generation in agile manner. Productionize when and where needed in partnerships with Tech@Lilly.
• Build serving infrastructure supporting both synchronous (interactive scientist queries) and asynchronous (batch and agent-invoked) workloads in partnership with Tech@Lilly and Frontier AI.
• Define and implement API contracts, documentation standards, and testing frameworks that ensure scientific tools are analysis ready, robust and consumable by external teams including Frontier AI.

Platform Engineering & Integration

• Build and operate cloud-native model serving infrastructure (AWS, Azure, or GCP) using containers, Kubernetes, and infrastructure-as-code.
• Develop CI/CD pipelines for ML models: automated validation, A/B testing, canary deployments, and rollback procedures.
• Integrate model serving with Data Foundry’s data pipelines, ensuring models have access to properly formatted, versioned training and inference data.

Frontier AI Interface & Collaboration

• Partner with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well-defined interfaces (REST APIs, MCP-compatible endpoints) that agents can invoke programmatically.
• Collaborate on API performance requirements: latency targets, throughput guarantees, and graceful degradation under load.
• Work with Methods4Insight scientists to ensure deployed models include appropriate uncertainty quantification and confidence metrics.

Basic Requirements

• B.S. or M.S. in Computer Science, Data Scie