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

UnitedHealth Group is a leading healthcare organization focused on improving health data flow and information systems. They are seeking a Senior AI/ML Engineer to design and implement AI systems for healthcare applications, with responsibilities including architecture ownership, fraud detection, and mentoring a distributed team.

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 & Collaboration: Review PRs, mentor junior engineers, and collaborate across India and US time zones in a distributed, agile environment

Requirements:

• Undergraduate degree or equivalent experience
• 5+ years of total engineering experience
• 5+ years of experience in AI/ML product engineering roles
• 5+ years of solid Python development experience; proficiency with ML frameworks such as PyTorch, scikit-learn, and Hugging Face
• 5+ years of experience with the Azure AI stack, including Azure OpenAI, Cognitive Services, Functions, Service Bus, and Cognitive Search
• 5+ years of experience with fraud detection, anomaly detection, risk scoring, or graph/network analytics pipelines
• 3+ years of solid experience with voice systems, including ASR, TTS, and real-time audio or telephony integration
• 2+ years of proven experience building and shipping LLM-powered or autonomous agent systems in production
• 2+ years of deep experience with LLM integration, tool calling, prompt engineering, and context-aware task execution
• 2+ years of hands-on experience with retrieval techniques such as RAG, semantic search, embeddings, and vector databases
• Proven solid SQL development skills, including complex joins, window functions, aggregations, and performance optimization for analytical workloads
• Experience working with healthcare claims, provider, enrollment, encounter, or other highly regulated transactional healthcare datasets
• Demonstrated ability to explain model behavior, risk signals, and analytic findings to nontechnical stakeholders through clear, defensible documentation
• Demonstrated track record of contributing to robust, testable, and scalable engineering systems
• Experience building automated evaluation harnesses for LLM and agent workflows, including golden datasets, offline and online testing, and measurable quality metrics such as task success rate, groundedness, or human-review agreement
• Hands-on experience with responsible AI, including prompt injection testing, data exfiltration testing, safety reviews, and guardrails to reduce hallucinations and unsafe outputs in production
• Proven experience implementing end-to-end observability for agentic systems, including distributed tracing, tool-call success rates, latency and error budgets, and token and cost telemetry with actionable alerting
• Proven experience designing secure patterns for tool-enabled agents, including least-privilege access, secrets management, and policy-based controls for tool or API execution such as OAuth scopes, managed identity, and audit logging
• 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
• Direct experience supporting Medicaid program integrity, healthcare fraud analytics, State Medicaid Agency analytics, or MCO SIU workflows
• Familiarity with Medicaid reimbursement and billing constructs, including ICD-10, CPT/HCPCS, DRGs, revenue codes, NDCs, and encounter data
• Familiarity with MMIS, T-MSIS, PERM, CMS program integrity guidance, or similar state or federal compliance frameworks
• Experience supporting referral, recovery, audit, appeal, or case-prioritization workflows
• Experience developing AI/ML solutions in regulated or government healthcare environments