Senior Technical Lead - AI Product Engineering (CMH Health)

Milliman is hiring a Senior Technical Lead for its CMH Health practice to own the technical direction and delivery of AI-enabled healthcare products. The role combines hands-on software engineering with architecture, production reliability, AI-assisted development, and technical leadership of other engineers.

Responsibilities:

• 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 our quality and risk management requirements

Requirements:

• 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
• Bachelor's degree in Computer science, Engineering, or a related field
• At least six years of relevant experience in software engineering, data-intensive systems, platform engineering, or closely related work
• At least two years in a technical lead, staff engineer, or similar role, including technical direction, delivery ownership, and developing others
• Strong production-software engineering skills, with the ability to inspect, debug, and improve implementations—not just describe proposed solutions
• Experience designing and operating cloud-based systems on Azure or a comparable platform, including deployment, identity and access, observability, and operational reliability
• Strong understanding of SQL, data modeling, APIs, and cross-system integration
• Demonstrated judgment about testing, data integrity, security boundaries, failure modes, and the evidence needed before releasing a change
• Practical experience using AI-assisted development tools, with a disciplined approach to validating their output
• Evidence of leading substantial technical work through delivery while managing dependencies, scope, and stakeholder expectations
• Clear written and verbal communication, constructive technical pushback, and a record of helping other engineers become more capable
• Curiosity about the business behind the software and a willingness to learn complex healthcare and actuarial workflows
• Azure services, infrastructure as code, CI/CD, or Databricks
• Healthcare analytics, claims data, actuarial systems, or another regulated, data-intensive domain
• Production AI-enabled workflows with evaluation datasets, traceability, and appropriate human review
• Modernizing established applications and data pipelines without disrupting users
• Reducing recurring support effort, release friction, or cloud operating costs through measurable improvements