Principal AI/ML Engineer – Market Access

Lilly USA, LLC is hiring a Principal AI/ML Engineer to build and operate agentic AI, machine learning, and data platforms for the Lilly Employer Organization. The role turns prototypes into secure, reliable production systems and develops reusable pipelines, integrations, and engineering foundations for employer-focused market access use cases.

Responsibilities:

• 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
• 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
• 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