Principal AI/ML Engineer – Market Access

What You'll Be Doing

• Design and build the engineering backbone for AI and ML systems: orchestration frameworks, end-to-end data and model pipelines, serving infrastructure, and evaluation and monitoring.
• Work at the intersection of employer data, benefit administration, field operations, and third-party partner integrations.
• Partner closely with data scientists to productionize models and agentic workflows.
• Work with the Employer Technical Product Manager, employer field teams, and Tech@Lilly colleagues to bring solutions into real applications in a secure and auditable way.
• Reuse infrastructure and patterns so 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.
• Engineer robust end-to-end AI/ML pipelines covering data capture, feature engineering, training, fine-tuning, inference, and feedback at production scale.
• Stand up ML engineering foundations including model and prompt versioning, registries, experiment tracking, CI/CD for models, and automated evaluation and rollback.
• Apply production-grade deployment techniques including scalable model serving, containerization, inference optimization, and deployment of small and specialized models hosted in-house.
• Build observability and monitoring for AI systems including tracing of agentic workflows, cost and latency tracking, drift detection, and AI evaluation integration.
• Implement governance and data-protection controls including HIPAA compliance, PHI and PII handling, de-identification, and auditability.
• Design reusable, horizontal engineering capabilities and shared infrastructure for multiple teams and use cases.
• 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, technical standards, project delivery, and the industry. Business analysis and technical leadership, including engaging with the business, translating business needs into technical terms, eliciting requirements, participating in design reviews, networking with Tech@Lilly colleagues, reusing existing processes and services, and staying abreast of tools and technologies.

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.

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, CI/CD for models, automated evaluation in pipelines, and observability for AI systems.
• Production deployment: experience with scalable model serving, containerization, inference optimization, 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, benefit design, payers, PBMs, and access models. Experience in healthcare or pharma is a plus.
• Demonstrated ability to deliver reliable systems in complex data domains and to communicate with a geographically dispersed group of business and technical colleagues.