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

Optum Insight is improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, and ultimately consumers. Our deep expertise in the industry and innovative technology empower us to help organizations reduce costs while improving risk management, quality and revenue growth. Ready to help us deliver results that improve lives? 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, operational signals, and network patterns 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 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, graph analytics, and explainable AI to detect emerging fraud, waste, and abuse across claims, enrollment, provider, and encounter data; uncover collusive networks and billing anomalies; and translate those signals into defensible investigative leads, preventive controls, 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 in a highly regulated healthcare environment, 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.

Primary 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 such as LangChain, AutoGen, and Semantic Kernel
• Modular Agent Design: Leverage standards such as Model Context Protocol (MCP) to define reusable, secure, and composable tool interfaces
• Voice-Driven Interfaces: Develop voice-first AI agents using ASR technologies such as Whisper and Azure Speech, multi-turn conversation orchestration, and high-quality TTS
• RAG & Memory Pipelines: Design and maintain retrieval and memory pipelines using vector databases and Azure Cognitive Search to ground agents in enterprise knowledge, prior interactions, and operational context
• Fraud, Waste & Abuse Analytics & ML Ops: Design, build, and operationalize supervised and unsupervised models, including classification, clustering, anomaly detection, risk scoring, and graph/network analysis, to detect known and emerging FWA patterns across claims, enrollment, provider, and encounter data. Translate fraud typologies such as upcoding, unbundling, excessive units, duplicate or phantom billing, kickbacks, and encounter discrepancies into scalable model logic, rules, and real-time detection pipelines. Continuously refine detection effectiveness using referral, audit, and recovery outcomes where available
• SQL & Data Engineering for FWA: Develop and optimize complex SQL queries, feature pipelines, and data validation checks for large-scale healthcare analytical workflows, including joins, window functions, aggregations, and performance-aware query design
• Cloud-Native AI Deployment: Build, deploy, and monitor scalable AI services on Azure, including Azure OpenAI, Functions, Service Bus, Cosmos DB, Cognitive Search, and related tools
• Explainability, Investigations & Governance: Produce clear, reproducible model outputs, narratives, visualizations, and KPI reporting that support investigators, clinicians, compliance teams, and business leaders. Contribute to model governance, validation, and documentation practices that ensure transparency, fairness, and regulatory defensibility
• 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 &