Lead AI/ML Engineer

UnitedHealth Group•Eden Prairie, MN

1d•$145,500 - $249,500•Remote

About The Position

The Lead AI/ML Engineer will lead the design, development, and deployment of AI/ML solutions for the Desktop of the Future platform and define enterprise AI architecture for capabilities such as AI Assist, agentic workflows, Next Best Actions, knowledge retrieval, call summaries, and intelligent automation. The role builds scalable agentic AI and Retrieval-Augmented Generation (RAG) solutions using large language models, orchestration frameworks, and enterprise knowledge sources. The engineer establishes AI governance, model evaluation, monitoring, observability, and responsible AI practices, and improves systems through model optimization, prompt engineering, performance tuning, and experimentation. The role works across Product, Architecture, Security, Engineering, UX, and Business teams; creates reusable AI frameworks and services; designs human-in-the-loop workflows and AI-to-human handoffs; and evaluates emerging AI technologies. This position provides technical leadership and mentorship to AI/ML engineers, software engineers, and data scientists, while setting best practices for AI engineering, MLOps, testing, deployment, scalability, and operations. Solutions must meet enterprise security, privacy, risk, and regulatory requirements, including HIPAA and PHI handling standards. The role may be performed remotely from anywhere in the U.S.; hires 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 in computer science, artificial intelligence, machine learning, engineering, data science, or a related field.
8+ years of software engineering experience, including 5+ years focused on AI/ML solution development.
3+ years serving as a technical lead, architect, or lead engineer for enterprise AI initiatives.
Proven experience designing, developing, and deploying AI/ML solutions in production environments.
Hands-on experience with LLMs such as GPT, Claude, Gemini, Llama, or similar technologies.
Experience building enterprise-scale RAG, semantic search, vector database, and knowledge retrieval solutions.
Solid proficiency in Python and modern AI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, PyTorch, or TensorFlow.
Experience developing AI orchestration, multi-agent systems, conversational AI, or intelligent workflow solutions.
Solid understanding of MLOps, model lifecycle management, AI observability, and production operations.
Experience with cloud-native AI solutions on Azure, AWS, or GCP.
Solid understanding of distributed systems, APIs, microservices, and enterprise software architecture.
Experience implementing secure, compliant, and scalable AI solutions in enterprise environments.
Excellent communication and stakeholder management skills, including the ability to influence technical and business leaders.
Success improving AI performance through prompt engineering, model tuning, retrieval optimization, inference optimization, and evaluation frameworks.
Experience improving AI quality metrics, including accuracy, latency, response relevance, hallucination reduction, user adoption, and business outcomes.
Experience implementing AI observability, monitoring, guardrails, feedback loops, and continuous learning frameworks in production.
Experience building and optimizing conversational AI, agent-assist platforms, copilots, virtual assistants, or digital employee experiences.
Experience integrating AI solutions with enterprise systems such as CRM, knowledge management, workflow orchestration, and case management platforms.
Experience building reusable enterprise AI platforms that support multiple business units and large-scale adoption.
Track record of transforming AI proof-of-concepts into scalable, production-ready enterprise solutions.

Nice To Haves

Healthcare industry experience, including payer, provider, pharmacy, care management, population health, or member services.
Understanding of HIPAA, PHI handling, healthcare workflows, and healthcare regulatory requirements.
Experience designing AI solutions for contact center, customer service, member service, or advocate desktop environments.
Familiarity with contact center technologies, telephony platforms, CRM systems, workforce optimization tools, and omnichannel engagement solutions.
Experience using AI to improve operational metrics such as Average Handle Time, First Contact Resolution, advocate productivity, knowledge retrieval efficiency, call summarization accuracy, Next Best Action recommendations, and customer and member satisfaction.
Experience with Azure OpenAI, Azure AI Foundry, Azure AI Search, and modern AI platform technologies.
Experience in highly regulated environments where security, governance, compliance, and auditability are critical.

Responsibilities

Lead the design, development, and deployment of AI/ML solutions powering the Desktop of the Future platform.
Define and implement enterprise AI architecture for AI Assist, agentic workflows, Next Best Actions, knowledge retrieval, call summaries, and intelligent automation.
Design and build scalable agentic AI solutions using LLMs, AI orchestration frameworks, and autonomous workflow capabilities.
Develop and optimize RAG solutions that provide accurate, contextual, and explainable responses from enterprise knowledge sources.
Establish AI governance, model evaluation, monitoring, observability, and responsible AI practices.
Improve AI capabilities through model optimization, prompt engineering, performance tuning, and experimentation.
Collaborate with Product, Architecture, Security, Engineering, UX, and Business teams to translate business needs into AI-enabled experiences.
Create reusable AI frameworks and services for multiple lines of business and enterprise products.
Design human-in-the-loop workflows, confidence scoring mechanisms, and AI-to-human handoff strategies.
Lead technical evaluation and adoption of emerging AI technologies, tools, and platforms.
Mentor and provide technical leadership to AI/ML engineers, software engineers, and data scientists.
Establish best practices for AI engineering, MLOps, testing, deployment, scalability, and operational excellence.
Ensure compliance with enterprise security, privacy, risk, and regulatory requirements, including HIPAA and PHI handling standards.