Principal AI​/ML Engineer - Market Access

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.

** Do you want to engineer the platforms that take agentic AI from prototype to production?*
* The Lilly Employer Organization is a critical priority for Lilly USA. It treats employers as first-class customers alongside payers and providers, and it scales Lilly's direct-to-employer (DTE) access models, benefit design innovation, and employer engagement, particularly for obesity and cardiometabolic medicines. The Lilly USA Tech@Lilly Market Access team is seeking a Principal AI/ML Engineer to build the agentic AI and machine learning platforms that power this work.

You will turn prototypes into reliable, scalable, well-governed production systems. You will also build the employer data pipelines, partner integrations, and ML engineering foundations that the team reuses across employer use cases.

** What You'll Be Doing:*
* As a Principal AI/ML Engineer, you will design and build the engineering backbone for the AI and ML systems that serve the Lilly Employer Organization: the orchestration frameworks, end-to-end data and model pipelines, serving infrastructure, and the evaluation and monitoring that keep them reliable in production. You will work at the intersection of employer data, benefit administration, field operations, and third-party partner integrations.

You will partner closely with data scientists to product ionize models and agentic workflows. You will also work with the Employer Technical Product Manager, employer field teams, and Tech@Lilly colleagues to bring those solutions into real applications in a secure and auditable way. You will reuse infrastructure and patterns so that each new employer workflow is faster and cheaper to ship.

** How You'll Succeed:*
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** Build and operationalize agentic AI platforms,
** including agent-to-agent orchestration frameworks, multi-agent workflow engines, tool and API integration layers, and secure connectivity between agents, enterprise systems, and external partner platforms.

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** Build and integrate employer data pipelines,
** bringing data from employers, benefit administrators, and third-party partners into secure, analytics-ready platforms.

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** Deliver AI capabilities for employer use cases,
** such as employer reporting, predictive analytics, benefit optimization, and decision support for employer field teams.

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** Engineer robust, end-to-end AI/ML pipelines,
** covering data capture, feature engineering, training, fine-tuning, inference, and feedback, at production scale and reliability.

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** Stand up ML engineering foundations,
** including model and prompt versioning, registries, experiment tracking, continuous integration and delivery for models, and automated evaluation and rollback within each pipeline.

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** Apply production-grade deployment techniques,
** including scalable model serving, containerization, inference optimization, and deployment of small and specialized models hosted in-house, with attention to latency, cost, scalability, and data residency.

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** Build observability and monitoring for AI systems,
** including tracing of agentic workflows, cost and latency tracking, drift detection, and integration of AI evaluation into production monitoring.

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** Implement governance and data-protection controls,
** including HIPAA compliance, PHI and PII handling, de-identification, and auditability consistent with a regulated environment.

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** Design reusable, horizontal engineering capabilities
** and shared infrastructure that multiple teams and use cases can build on, rather than one-off deployments.

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** Partner with data scientists
** to product ionize prototypes and harden them for scale, reliability, and maintainability.

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** Communicate clearly
** with technical and non-technical stakeholders and stay current with emerging agentic and ML engineering tooling.

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** Manage and prioritize multiple projects simultaneously.*
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