Principal AI/ML Engineer – Market Access
Lilly · United States, Indianapolis IN
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 productionize 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:
- 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.
- Build and integrate employer data pipelines, bringing data from employers, benefit administrators, and third-party partners into secure, analytics-ready platforms.
- Deliver AI capabilities for employer use cases, such as employer reporting, predictive analytics, benefit optimization, and decision support for employer field teams.
- Engineer robust, end-to-end AI/ML pipelines, covering data capture, feature engineering, training, fine-tuning, inference, and feedback, at production scale and reliability.
- 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.
- 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.
- 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.
- Implement governance and data-protection controls, including HIPAA compliance, PHI and PII handling, de-identification, and auditability consistent with a regulated environment.
- Design reusable, horizontal engineering capabilities and shared infrastructure that multiple teams and use cases can build on, rather than one-off deployments.
- Partner with data scientists to productionize prototypes and harden them for scale, reliability, and maintainability.
- Communicate clearly with technical and non-technical stakeholders and stay current with emerging agentic and ML engineering tooling.
- Manage and prioritize multiple projects simultaneously.
What You Should Bring:
- Foundational knowledge in communication, leadership, teamwork, problem solving, solution and blueprint definition, business acumen, architectural processes (e.g., blueprinting, reference architecture, governance), technical standards, project delivery, and the industry.
Business Analysis and Technical Leadership, including:
- Engaging with the business and proactively seeking opportunities to deliver business value.
- Understanding business requirements and effectively translating business needs and processes into technical terms, and vice versa.
- Eliciting and defining requirements.
- Ensuring appropriate business roles are engaged in solution execution.
- Participating in design reviews to ensure traceability of requirements.
- Networking with appropriate Tech@Lilly colleagues to determine solutions that meet business partners' needs.
- Seeking opportunities to reuse existing processes and services to streamline support and implementation of key systems.
- Staying abreast of tools and technologies to influence Tech@Lilly strategy so that it provides the best usage opportunities for the business.
Basic Qualifications:
- Master’s degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, or another quantitative or technical field, and 2 + years of experience developing, deploying, or maintaining AI, machine learning, or data systems in production environments.
- OR Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, or another quantitative or technical field, and 4 + years of experience developing, deploying, or maintaining AI, machine learning, or data systems in production environments.
- Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.
Preferred Qualifications:
- Strong proficiency in Python and sound software-engineering practices, including version control, testing, and code review.
- Experience building and deploying data or ML systems in production.
- Agentic platforms: experience engineering agent-to-agent orchestration and multi-agent workflow frameworks with modern tooling (e.g., LangGraph, LangChain, or equivalent), including tool and API integration and secure agent connectivity.
- End-to-end ML engineering: experience with model and prompt versioning, registries, experiment tracking, continuous integration and delivery for models, automated evaluation in pipelines, and observability for AI systems.
- Production deployment: experience with scalable model serving, containerization, inference optimization, and deployment and serving of large models and small, specialized models, plus fine-tuning and distillation pipelines.
- Data engineering: experience building scalable data pipelines, SQL and NoSQL data stores, distributed processing, vector databases, and retrieval systems.
- Cloud and tooling: experience with AWS or Azure, Databricks, containers, Git, and CI/CD automation.
- Governance: experience with secure, auditable systems, PHI and PII handling, and data-residency constraints.
- Partner integrations: experience integrating data and services from external partners, such as benefit administrators or health platforms, through APIs and secure data exchange.
- Domain: understanding of U.S. employer healthcare, including employer-sponsored benefits, benefi