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

UnitedHealth Group•Minnetonka, MN

4d•$98,500 - $176,000•Remote

About The Position

Optum Tech’s Provider Digital Technology team creates a unified digital entry point for UnitedHealthcare providers and partners to access tools, services, and information across Portal, API, EDI, Chat, and Voice channels. The AI/ML engineering team develops production-grade intelligent solutions, including generative AI assistants, retrieval-augmented generation (RAG), agentic workflows, conversational AI, predictive models, and intelligent automation. The team integrates enterprise applications and data sources while applying responsible AI, privacy, security, safety, observability, and human-centered design principles to reduce friction and improve provider workflows. The AI/ML Engineer will lead responsible adoption of AI/ML and build, evaluate, deploy, and improve scalable production services, APIs, and applications. The role provides technical leadership across agentic and conversational AI solutions and partners with product, architecture, data science, platform, and engineering teams to turn health care needs into secure, reliable solutions aligned with enterprise AI strategy. The position also establishes engineering practices and mentors engineers in AI/ML architecture and operations. The position may be performed remotely anywhere in the United States or office-based in Minnesota, Washington, D.C., or New Jersey. 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. Applicants must be authorized to work in the United States without current or future employer-sponsored visa sponsorship. The stated annual salary range is $98,500–$176,000 for full-time employment, subject to factors including location, education, work experience, and certifications.

Requirements

At least 2 years of hands-on experience with chatbots, conversational AI, NLP, RAG, embeddings, vector databases, and semantic search patterns.
At least 3 years of programming experience in Python or Java, including clean code, automated testing, source control, and modern software engineering practices.
At least 1 year of hands-on experience with Agentic AI, tool-calling workflows, MCP or similar orchestration frameworks, and Strands Agents.
Must be authorized to work in the United States without the need for current or future employer-sponsored visa sponsorship.

Nice To Haves

Knowledge of AI/ML production operations, including security, access controls, responsible AI guardrails, evaluation, logging, monitoring, performance tuning, disaster recovery, and incident support.
Experience delivering production AI/ML solutions in health care or another highly regulated industry.
Full-stack engineering experience and familiarity with modern web frameworks, cloud-native architectures, and infrastructure-as-code.
Experience with LLM evaluation, prompt testing, RAG quality measurement, model monitoring, and AI observability.
Experience with MLOps or LLMOps, CI/CD, containers, Kubernetes, feature or model lifecycle management, and production telemetry.
Knowledge of data engineering, vector stores, enterprise search, and secure integration of structured and unstructured data sources.

Responsibilities

Lead responsible adoption of AI/ML across the team, applying generative AI, agentic AI, NLP, predictive modeling, and traditional machine learning to provider use cases.
Design, build, evaluate, deploy, and continuously improve production-grade AI/ML services, APIs, and applications to meet customer, business, reliability, and scalability needs.
Engineer responsible AI solutions with guardrails, privacy and security controls, human oversight, auditability, and compliance with enterprise and regulatory standards.
Develop LLM-powered capabilities using prompt engineering, RAG, embeddings, vector search, tool use, structured outputs, and model evaluation techniques.
Provide technical leadership for agentic AI, chatbots, voice assistants, conversational AI, and workflow automation, including orchestration through MCP or similar frameworks.
Partner with product managers, architects, data scientists, platform teams, and engineering leaders to translate health care needs into scalable AI solutions.
Align solution architecture with enterprise AI strategy, reusable platform capabilities, engineering standards, cloud modernization, and measurable business outcomes.
Establish engineering practices for experimentation, automated testing, model and prompt evaluation, CI/CD, observability, performance, resiliency, and production support.
Mentor engineers on AI/ML architecture, Python and Java development, responsible AI, LLM application patterns, evaluation frameworks, and operational excellence.
Integrate AI capabilities with enterprise data, APIs, event-driven services, cloud platforms, and digital channels while maintaining secure and reliable data flows.
Use independent judgment to prioritize technical work, manage delivery risks, communicate tradeoffs, and drive AI initiatives from proof of concept through production scale.