VP AI ML Engineering, Medicare & Retirement

Vice President Of Ai/Ml Engineering For Medicare & Retirement

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives.

Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.

This leader translates complex healthcare business challenges-such as benefit design, clinical operations, member engagement, and sales optimization-into deployable AI solutions, platforms, and products. The role will lead enterprise AI/ML engineering at scale, embedding AI across plan design, digital experiences, and operational workflows.

The impact of this role is direct and measurable: driving revenue growth and retention, increasing market responsiveness, strengthening resiliency and compliance, and reducing cost to serve through AI first modernization, platform reuse, and operational excellence-while modeling United Health Group values of Integrity, Quality, Inclusion, Compassion, Relationships, Innovation, and Performance.

You'll enjoy the flexibility to work remotely
* from anywhere within the U.S. as you take on some tough challenges.

For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Primary Responsibilities:

• Define and execute an enterprise AI/ML strategy aligned to M&R business priorities and growth objectives
• Embed AI across core value streams: benefit design, member lifecycle, and clinical operations
• Drive transition from experimentation to scaled, production-grade AI across the portfolio

• Build and scale enterprise AI/ML platforms supporting model development, deployment, and monitoring
• Enable real-time decisioning and batch analytics at scale
• Standardize MLOps, AI pipelines, and engineering delivery practices
• Drive reusable AI services and shared capabilities across the enterprise
• Accelerate adoption of GenAI, LLMs, and agent-based systems where appropriate

• Partner with Product, Business (M&R leadership), and Finance to prioritize high-value AI use cases
• Align AI investments to measurable ROI outcomes across growth, clinical, and operational domains
• Translate business problems (pricing, engagement, operations) into deployable AI solutions
• Scale pilots into enterprise-grade platforms

• Lead delivery of production-grade AI systems with high reliability, scalability, and compliance
• Establish best practices for model validation, testing, and monitoring
• Optimize performance and cost efficiency across AI workloads and infrastructure
• Embed AI into SDLC and operational workflows as a standard engineering capability

• Lead AI/ML engineering across the M&R portfolio, enabling:
• Benefit design optimization
• Ancillary benefit optimization and validation
• DSNP and food benefit innovation
• Clinical and Medicare STARS automation
• Deliver measurable outcomes including:
• Revenue growth and marketing effectiveness
• Medical cost optimization and risk adjustment
• Cost-to-serve reduction through automation
• Improved Medicare STARS quality performance

• Ensure AI solutions comply with healthcare regulations and data privacy standards
• Establish responsible AI practices including fairness, explainability, and auditability
• Partner with security and risk teams to govern models and manage lifecycle risk

• Lead the AI Blueprint transformation for M&R Technology, converting enterprise AI priorities into a clear roadmap, governance model, and execution plan
• Align AI priorities and investments to measurable business value
• Drive scaled adoption of AI-first engineering practices, including AIDLC, reusable AI capabilities, responsible AI controls, and enterprise tooling
• Advance the M&R AI talent and workforce strategy through role-based enablement, adoption metrics, and operating model changes that build sustained AI capability
• Monitor transformation progress, risks, dependencies, and outcomes through executive reporting tied to value,…