Lead AI/ML Platform Engineer - Remote

UnitedHealth Group•Eden Prairie, MN

4d•$145,500 - $249,500•Remote

About The Position

Optum Tech’s Enterprise AI organization builds platforms that govern and operate AI systems across the business, including intake and registration, automated governance gating, request routing, and runtime observability and monitoring. The organization is connecting these capabilities end-to-end so pre-production governance links with post-production monitoring, alerting, feedback, and containment, supporting enterprise AI risk management commitments and audit-related deliverables. In this role, you will help design and build an end-to-end AI lifecycle governance platform. You will establish technical integrations, connect pre-production gating to runtime observability, and implement core integration components hands-on. You will collaborate across intake, governance, routing, and monitoring teams to define data contracts, set technical standards, and build a foundation for responsible AI at enterprise scale. The role is remote within the U.S. Employees hired in the Minneapolis or Washington, D.C. area must work in the office at least four days per week. Remote employees must follow UnitedHealth Group’s Telecommuter Policy.

Requirements

Bachelor’s degree or at least 4 years of software engineering or AI/ML engineering experience in lieu of a degree.
At least 5 years of hands-on AI/ML engineering or software engineering experience, including building and deploying production-level AI systems.
At least 3 years of experience designing multi-component distributed systems or platform architectures.
At least 3 years of professional experience developing production-grade services and integrations using Python.
At least 2 years of experience designing or implementing systems involving governance, runtime monitoring, observability, or lifecycle management of software or AI systems.
At least 2 years of experience leading technical design or guiding engineering teams across complex technical initiatives.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical discipline.
Experience with AI observability, model evaluation (evals), or testing frameworks and platforms.
Experience with automated governance gating, policy-as-code, or compliance automation for AI systems.
Experience developing within cloud-native architectures such as Azure, AWS, or GCP and containerized environments such as Docker or Kubernetes.
Familiarity with human-in-the-loop review, feedback mechanisms, and containment workflows for AI systems.
Familiarity with enterprise AI audit or risk management frameworks.

Responsibilities

Design, develop, and deploy AI-powered solutions for complex business challenges, emphasizing responsible AI use.
Architect and implement end-to-end AI lifecycle governance capabilities linking intake and registration, automated governance gating, request routing, and runtime observability and monitoring.
Design technical integrations connecting pre-production governance with post-production monitoring, alerting, feedback, and containment.
Define data flows, interfaces, and integration contracts between system components to support lifecycle traceability and compliance.
Build and deploy core integration components through hands-on engineering, automated testing, and low-level design.
Partner with and guide cross-functional engineering teams across intake, governance, routing, and observability to align technical designs and execution sequencing.
Use enterprise-approved AI tools to streamline workflows, automate tasks, and drive continuous improvement.
Evaluate emerging trends, platforms, and frameworks to inform solution design, continuous improvement, and strategic innovation.