Lead Software Engineer

UnitedHealth Group•Eden Prairie, MN

2d•$112,700 - $193,200•Remote

About The Position

Optum is a global organization that delivers care, aided by technology, to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. We are seeking a Lead Software Engineer to drive development and delivery of the next generation of scalable enterprise software and AI-powered applications in the Clinical AI domain. In this role, you will join a forward-thinking engineering team dedicated to building highly performant, secure, and automated solutions for AI 10.0 initiatives, including Touchless Prior Authorization. You will have the opportunity to work with modern technologies and cloud environments to integrate intelligent capabilities, automate workflows, and design robust architectures that solve complex business and healthcare challenges. You will enjoy the flexibility to telecommute from anywhere within the U.S. and be rewarded and recognized for your performance as you take on meaningful and complex challenges in healthcare.

Requirements

Bachelor's degree in science or technology, or equivalent experience
7+ years of experience in application architecture, AI/ML engineering, quality engineering, or platform engineering
5+ years of experience leading development/testing of with web, API, integration, or cloud-native solution development
5+ years of experience providing technical leadership to engineers, including mentoring, design review, delivery oversight, and best-practice adoption
4+ years of experience with CI/CD, DevOps tooling, release automation, and modern software delivery practices

Nice To Haves

Master’s degree in Information Technology or related field
Strong understanding of Generative AI, responsible AI, AI governance, model integration patterns, and enterprise AI enablement practices, demonstrated through certifications
Hands-on experience building or enabling AI/ML-powered engineering workflows, including use of AI to improve SDLC productivity and operations
Strong technical foundation in cloud architecture (GCP, Azure), APIs, distributed systems, event-driven architecture, security, identity, logging, observability, performance tuning, and production operations

Responsibilities

Lead end-to-end technical integration efforts for AI-enabled solutions, from opportunity discovery and architecture review through implementation, validation, launch, and operational handoff
Collaborate cross-functionally with business, product, engineering, security, infrastructure, and operational partners to identify AI integration opportunities that improve automation, workflow intelligence, decision support, and delivery speed
Translate business and operational needs into scalable technical requirements, ensuring solutions align with enterprise architecture standards, stakeholder goals, and responsible AI expectations
Design and deliver advanced AI-enabled integration solutions, including intelligent orchestration and AI-assisted workflows
Drive AI 10.0 initiatives by creating scalable, high-quality implementations that reduce manual effort, improve operational consistency, and accelerate delivery of critical automation work
Assess internal and external solution options for fit within enterprise infrastructure, including compatibility, scalability, security, reliability, data handling, observability, supportability, and long-term maintainability
Independently manage complex technical workstreams, proactively identifying dependencies, risks, blockers, governance needs, and decisions required to move initiatives from concept to production
Coordinate technical validation activities, including proofs of concept, environment readiness, API testing, performance evaluation, security reviews, AI workflow assessment, and production readiness
Ensure solutions meet enterprise standards for identity and access management, data privacy, compliance, resilience, monitoring, incident response, change management, auditability, and responsible AI use
Mentor engineers and influence best practices for AI-enabled solution design, automation, AI-assisted engineering, sustainable platform integration, operational readiness, and continuous improvement
Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI