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About This Role
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.
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 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
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:
- 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
Preferred Qualifications:
- 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
- All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far\-reaching choice of benefits and incentives. The salary for this role will range from $120,118 to $214,496 annually based on full\-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
*At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone\-of every race, gender, sexuality, age, location and income\-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes \- an enterprise priority reflected in our mission.*
*UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.*
*UnitedHealth Group is a drug \- free workplace. Candidates are required to pass a drug test before beginning employment.*
Salary Context
This $120K-$214K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Optum, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $120K to $214K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Optum AI Hiring
Optum has 23 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, MLOps Engineer. Positions span Sheffield, OH, US, Eden Prairie, MN, US, Hartford, CT, US. Compensation range: $74K - $273K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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