Sr AI/ML Engineer - Remote Nationwide or Hybrid in MN/DC

UnitedHealth Group•Eden Prairie, MN

4d•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. Come build the AI foundation behind SGS, a modern platform where multi agent systems can reason, plan, and safely connect to enterprise tools to turn conversations, documents, and network signals into action. In this role, you will own the architecture for the capabilities customers feel every day, including real time voice translation, voice based assessments, and call quality audits that help teams respond faster and follow the right procedures. You will also advance Document AI and our Virtual Investigator to accelerate intake, surface risk early, and turn complex investigative questions into clear, explainable answers. On the fraud side, you will combine real time ML with graph analytics to detect emerging hotspots, uncover collusive networks, and stop improper payments before they happen, then translate that intelligence into AI generated insights and KPI visibility leaders can use immediately. You will set the technical direction for orchestration and governance on Azure, raise engineering standards across product lines, and mentor a globally distributed team that ships to production. If you want deep ownership, high stakes impact, and the chance to define how agentic AI operates at scale, you will thrive here. 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.

Requirements

Undergraduate degree or equivalent experience
5+ years of total engineering experience
5+ years of experience with AI/ML product engineering roles
5+ years of experience with Python development; proficiency with ML frameworks like PyTorch, scikit-learn, Hugging Face
5+ years of experience with Azure AI stack (Azure OpenAI, Cognitive Services, Functions, Service Bus, Cognitive Search)
5+ years of experience with fraud detection models or anomaly detection pipelines
3+ years of experience with voice systems, including ASR, TTS, and real-time audio/telephony integration
2+ years of experience with LLM integration, tool calling, prompt engineering, and context-aware task execution
2+ years of experience building and shipping LLM-powered or autonomous agent systems in production
2+ years of hands-on work experience with retrieval techniques (RAG, semantic search, embeddings) and vector DBs
Demonstrated track record of contributing to robust, testable, and scalable engineering systems

Nice To Haves

LLM evaluation and regression testing experience: experience building automated evaluation harnesses for LLM and agent workflows, including golden datasets, offline and online testing, and measurable quality metrics (for example task success rate, groundedness, or human review agreement)
Responsible AI and adversarial testing experience: Hands on experience with prompt injection and data exfiltration testing, safety reviews, and implementing guardrails to reduce hallucinations and unsafe outputs in production
Production observability for agents: Proven implemented end to end monitoring for agentic systems, including distributed tracing, tool call success rates, latency and error budgets, and token and cost telemetry with actionable alerting
Security for tool calling and AI systems experience: Proven designed secure patterns for tool enabled agents, including least privilege access, secrets management, and policy based controls for tool/API execution (for example OAuth scopes, managed identity, and audit logging)
Platform scale and efficiency experience: Proven ability to optimize LLM or voice system performance and cost using techniques such as caching, batching, streaming responses, rate limiting, model routing, and fallback strategies

Responsibilities

AI Engineering Projects: Design and implement multi-agent AI systems that use LLMs, memory, and tools to reason, plan, and act autonomously
Function Calling & Orchestration: Build agent-based solutions that use function calling, dynamic tool integration, and orchestration frameworks like LangChain, AutoGen, and Semantic Kernel
Modular Agent Design: Leverage standards like Model Context Protocol (MCP) to define reusable, secure, and composable tool interfaces
Voice-Driven Interfaces: Develop voice-first AI agents using ASR (e.g. Whisper, Azure Speech), multi-turn conversation orchestration, and high-quality TTS
RAG & Memory Pipelines: Design and maintain Retrieval-Augmented Generation (RAG) pipelines using vector databases and Azure Cognitive Search
Fraud Detection & ML Ops: Contribute to anomaly detection and fraud modeling efforts, including ML model training, real-time inference, and pipeline optimization
Cloud-Native AI Deployment: Build, deploy, and monitor scalable AI services on Azure, including Azure OpenAI, Functions, Service Bus, Cosmos DB, and related tools
Agentic UX & AI as an Interface: Drive innovation in agentic user experiences, enabling AI to operate external tools and services securely on behalf of users
Mentorship & Collaboration: Review PRs, mentor junior engineers, and collaborate across India and US time zones in a distributed, agile environment