Senior AI Product Manager; CMH Health

Individual(s) must be legally authorized to work in the United States without the need for immigration support or sponsorship from Milliman now or in the future.

Milliman’s CMH Health practice is seeking an experienced technical leader who can turn challenging business workflows into reliable software—and help other engineers do the same.

As a Senior AI Product Manager you will own technical direction and deliver quality for assigned products or cross-system work streams. You will work closely with product owners, engineers, and healthcare subject-matter experts to make sound architectural decisions, strengthen verification and release practices, and remove recurring obstacles to delivery.

This is a technically engaged leadership role, not a position focused primarily on meeting coordination or architecture documents. You should be comfortable examining code, investigating production behavior, and building critical components. Your broader impact will come from guiding engineers and AI-assisted development tools, creating reusable solutions, and developing stronger technical ownership throughout the team.

Founded in 1947, Milliman is an independent firm that provides actuarial, risk management, technology, data, and economic consulting. Health plans, employers, pharmacies, PBMs, life sciences companies, and government agencies rely on our people and products to make high-stakes financial and clinical decisions.

Our CMH Health AI Product & Engineering team turns decades of Milliman healthcare expertise into software. We build and run commercial products that clients use in pricing, contracting, benefit design, regulatory work, and competitive intelligence. We are moving from selling expertise by the hour to selling scalable intellectual property, and we use generative AI throughout the way we design, build, and deliver.

• Lead pragmatic architecture and integration. Design service boundaries, APIs, data models, and integrations that balance reliability, security, performance, cost, and maintainability. Explain the tradeoffs and choose the simplest approach that meets the need.
• Turn business needs into executable technical plans. Work with product owners and domain experts to clarify requirements, identify dependencies, and sequence small, testable increments. Own technical delivery commitments and raise scope, budget, or timing risks early.
• Build verification into delivery. Strengthen automated tests, data-quality controls, regression checks, and AI evaluation practices. Ensure evidence demonstrates that the complete workflow works—not just that individual components run.
• Improve production readiness and operability. Establish practical deployment, monitoring, rollback, and support practices. Investigate recurring failures and reduce manual intervention through targeted engineering improvements.
• Make AI-assisted engineering effective and accountable. Guide the use of coding agents and other AI tools, including context management, generated-code review, evaluation, and appropriate human oversight. Measure improvements in complete delivery outcomes rather than code volume.
• Create reusable engineering capabilities. Improve shared patterns and tooling where they solve demonstrated needs. Help teams adopt them without introducing unnecessary abstraction or approval bottlenecks.
• Develop independent engineers. Delegate meaningful ownership, review designs and implementations, provide timely feedback, and coach others through difficult technical decisions.
• Connect engineering decisions to user outcomes. Stay close to consultants and users, support rollout and adoption, and explain technical risks clearly. Partner with the appropriate domain and control authorities to meet…