Senior AI/ML Engineer

UnitedHealth Group•Eden Prairie, MN

9h•$120,100 - $214,500•Remote

About The Position

Transform Healthcare Through AI Innovation at Optum. Optum is a global organization delivering care, powered by data and technology, to help millions of people live healthier lives. At Optum.ai, we are not just witnessing the AI transformation in healthcare—we are leading it. Our mission is clear: to simplify healthcare with AI, turning insight into action at a scale few organizations in the world can match. As part of the Optum.ai team, you’ll work at the intersection of cutting-edge artificial intelligence and real-world healthcare impact. From reducing administrative burden for providers to anticipating patient needs and improving access to quality care, your work will help solve some of healthcare’s most complex challenges—and directly improve health outcomes for millions of people. You’ll collaborate with world-class talent across data science, engineering, product, and healthcare domains, backed by the reach and stability of Optum and UnitedHealth Group. Here, responsible innovation matters. So do comprehensive benefits, meaningful career growth, and the opportunity to make a tangible difference—advancing health equity and creating a simpler, more connected healthcare experience for everyone. This is more than a job. It’s a chance to shape the future of healthcare through the transformative power of AI. Join us to start Caring. Connecting. Growing together. Optum AI is UnitedHealth Group’s enterprise AI team. We are AI/ML scientists and engineers with deep expertise in AI/ML engineering for healthcare. We develop AI/ML solutions for the highest impact opportunities across UnitedHealth Group businesses including UnitedHealthcare, Optum Financial, Optum Health, Optum Insight, and Optum Rx. In addition to transforming the healthcare journey through responsible AI/ML innovation, our charter also includes developing and supporting an enterprise AI/ML development platform.

Requirements

5+ years of experience in machine learning, AI engineering, or applied data science including development of production ML systems
5+ years of experience developing Python-based ML systems using frameworks such as PyTorch, TensorFlow, or Hugging Face
3+ years of experience building Generative AI or LLM applications including prompt engineering, model fine‑tuning, and LLM API integrations
2+ years of experience designing retrieval‑augmented generation (RAG) systems and vector search pipelines
1+ year of experience building agentic AI systems including tool‑calling, planning frameworks, or multi‑agent workflows
1+ year of experience using AI‑assisted development or 'vibe coding' tools such as Codex, Claude Code, Cursor, Windsurf, or similar tools
Healthcare Experience
Must be authorized to work in the United States without the need for current or future employer-sponsored visa sponsorship (e.g., H-1B, TN, F-1/OPT, CPT, or other employment-based visa status)

Nice To Haves

Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel
Experience optimizing LLM latency, cost, and performance in production environments
Experience deploying AI systems on cloud platforms such as AWS, Azure, or GCP
Experience working in regulated environments with responsible AI governance requirements

Responsibilities

Design and deploy generative AI and NLP solutions using transformer-based architectures for enterprise use cases such as search, summarization, extraction, conversational AI, and decision support
Develop and fine-tune large language model (LLM) applications using commercial and open-source models such as OpenAI, Anthropic/Claude, Gemini, LLaMA, or Mistral
Design and implement retrieval‑augmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding generation, and vector database integration
Build agentic AI systems including single‑agent and multi‑agent workflows capable of tool use, reasoning, planning, and autonomous task execution
Implement evaluation and monitoring frameworks for generative AI systems including hallucination detection, bias monitoring, and human‑in‑the‑loop evaluation
Develop safety and governance controls for AI systems including prompt hardening, policy enforcement, and responsible AI guardrails
Productionize AI/ML pipelines using CI/CD, testing, monitoring, and observability best practices in cloud‑native environments
Collaborate with product, platform, and data science teams to translate requirements into scalable AI solutions