Director, AI ML Engineering - Remote

UnitedHealth Group•Schaumburg, IL

19h•$134,600 - $230,800•Remote

About The Position

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. 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.

Requirements

Bachelor’s degree or equivalent practical experience in applied AI, technology strategy, or complex enterprise systems
10+ years of experience delivering and leading complex technology initiatives, with demonstrated expertise either through hands‑on software engineering experience OR through deep technical partnership roles, including the driving or ownership of complex systems, platforms, or large‑scale transformation initiatives
Experience connecting AI initiatives to measurable business value, using data, analytics, and outcome‑based metrics
Demonstrated experience designing, building, or operationalizing AI‑powered solutions, including agent‑based or LLM‑driven systems applied in real‑world environments
Deep understanding of modern AI techniques and platforms, including large language models and agent frameworks used to accelerate software development and analysis
Proven ability to identify high‑value AI opportunities, translate them into clear use cases, and support delivery through collaboration with engineering teams

Nice To Haves

Experience in PBM, U.S. Healthcare and/or RxClaim platform
Experience designing, building, or leading agentic AI or multi‑agent systems deployed in production environments
Familiarity with modern SDLC and DevOps practices and how AI can accelerate areas such as testing, code quality, deployment, operations, and reliability
Background in platform, enablement, or productivity teams, driving adoption of shared capabilities at scale
Demonstrated success leading organizational change, moving teams from experimentation to sustained, value‑driven technology adoption
Proven excellent communication and leadership skills, with the ability to operate credibly across engineering, product, and executive stakeholders

Responsibilities

Define and own the engineering AI strategy to measurably accelerate software development, quality, reliability, and operational efficiency through the use of AI agents
Partner with engineering teams to analyze current workflows, development activities, and pain points across the SDLC, identifying high‑value opportunities where AI agents can deliver meaningful impact
Develop a clear, prioritized roadmap of AI agent use cases aligned to business objectives, engineering strategy, and enterprise technology standards
Build data‑driven proposals and business cases for AI agent investments, including baseline measurements, expected productivity gains, cost avoidance, quality improvements, and adoption metrics
Lead the design, development, and deployment of AI agents that augment or automate engineering work (e.g., requirements analysis, design support, coding, testing, deployment, reliability, incident analysis)
Oversee the end‑to‑end lifecycle of AI agents from proof of value through scaled production deployment, ensuring solutions are reliable, secure, observable, and maintainable
Establish measurement frameworks, analytics, and KPIs to track realized value and continuously refine AI solutions based on usage, outcomes, and feedback
Define a repeatable operating model for how engineering teams identify, build, govern, and adopt AI agents at scale
Ensure all AI solutions adhere to enterprise architecture, security, privacy, regulatory, and responsible AI standards, including appropriate human‑in‑the‑loop controls
Drive engineering enablement and change management, helping teams adopt new AI‑enabled ways of working and build confidence using AI as a force multiplier
Serve as a trusted advisor and thought leader to engineering and technology leadership, translating complex AI concepts into clear decisions, trade‑offs, and business outcomes
Collaborate closely with architecture, platform, product, data, and security partners to align AI agent strategy with broader enterprise initiatives