Lead AI/ML Engineer Remote Nationwide or Office-Based in MN/DC

UnitedHealth Group•Eden Prairie, MN

15d•$145,500 - $249,500•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. AI/ML Engineers design, build, and support software solutions that apply artificial intelligence, machine learning, analytics, and automation to solve business problems. Work may include model development, generative AI, data-driven applications, evaluation, deployment, monitoring, and continuous improvement. This role emphasizes sound engineering practices, collaboration, responsible AI, and translating business needs into scalable and reliable technology solutions Reviews architecture, design, code, model outputs, evaluation results, and production readiness artifacts created by others Develops innovative and reusable approaches that accelerate enterprise adoption of AI/ML, GenAI, agentic AI, and intelligent automation Is sought out as a subject matter expert for AI/ML architecture, responsible AI, LLMOps, model evaluation, AI security, and production-grade AI delivery Serves as a technical leader and mentor, enabling engineers across teams through standards, design reviews, coaching, hands-on guidance, and knowledge sharing You’ll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Requirements

10+ years of professional experience in software engineering
4+ years of hands-on experience designing, building, deploying, or supporting AI/ML, analytics, or automation solutions
4+ years of hands-on experience in Python
2+ years of experience providing technical leadership, mentoring engineers, leading solution design, or guiding cross-functional delivery
2+ years of experience working with generative AI, large language models, AI agents, intelligent automation, or related emerging AI capabilities
2+ years of experience with cloud platforms, production deployment practices, data platforms, or enterprise engineering environments
2+ years of experience with production delivery practices such as monitoring, automated deployment, CI/CD, operational support, or continuous improvement
2+ years of experience applying responsible AI, data privacy, security, governance, or compliance practices in enterprise environments
2+ years of experience evaluating AI/ML solutions using quality, performance, reliability, and business outcome measures

Nice To Haves

Experience delivering AI/ML solutions in large-scale enterprise or regulated environments
Experience translating emerging AI/ML techniques into practical, production-ready solutions
Experience defining evaluation approaches for AI/ML quality, performance, safety, or business value
Experience driving adoption of reusable AI/ML platforms, patterns, standards, or engineering practices across teams
Experience partnering with product, business, and technology stakeholders to shape AI/ML roadmaps and prioritize use cases
Experience in healthcare, financial services, government, or other regulated enterprise domains
Contributions to open-source AI frameworks, patents, publications, or applied AI thought leadership

Responsibilities

Lead the design, development, and delivery of enterprise AI/ML solutions that address complex business and technology needs
Develop and operationalize AI/ML capabilities, including machine learning models, generative AI applications, analytics solutions, and intelligent automation
Provide technical leadership for solution design, architecture decisions, engineering practices, and delivery approaches across AI/ML initiatives
Apply appropriate AI, machine learning, data, and software engineering techniques to build scalable, reliable, and maintainable solutions
Support modern AI/ML delivery practices, including experimentation, deployment, monitoring, governance, and continuous improvement
Help ensure AI/ML solutions are production-ready, observable, secure, compliant, and aligned with enterprise standards
Work with stakeholders to define success measures, evaluate outcomes, and ensure AI/ML solutions deliver measurable business value
Evaluate and improve AI/ML solutions for performance, quality, scalability, maintainability, and cost-effectiveness
Collaborate on data, feature, and retrieval pipelines that support AI/ML development, evaluation, deployment, and monitoring
Partner with product, business, data, platform, security, compliance, operations, and engineering teams to deliver AI/ML capabilities in enterprise environments
Promote reusable patterns, shared components, engineering standards, documentation, and responsible AI practices across teams
Apply responsible AI, risk management, privacy, security, and compliance considerations throughout the AI/ML lifecycle
Identify technical risks, communicate trade-offs, and guide practical mitigation approaches for AI/ML solutions
Select appropriate techniques and tools based on business needs, technical feasibility, quality expectations, and operational considerations
Balance innovation with practical delivery by choosing reliable, explainable, maintainable, and cost-effective approaches
Mentor engineers, review technical work, share knowledge, and help raise engineering quality across AI/ML initiatives
Communicate technical concepts, recommendations, risks, and outcomes clearly to technical and non-technical audiences