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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.
We are seeking a highly skilled Solution Architect focused on Digital, AI \& Data Transformation. Architect will lead the design, development, and implementation of next\-generation Optum Bank's Digital, data and AI systems. This role combines deep hands\-on engineering with strategic architecture, focusing on modern data transformation pipelines, small language models (SLMs), AI frameworks, and AI/ML solutions such as fraud detection, lending underwriting models, Operational Agents, and others.
The ideal candidate will influence enterprise\-wide engineering practices, using tools like Claude Code, GitHub Copilot, Codex, and AI\-driven SDLC automation to significantly elevate developer productivity, velocity, code quality, and application modernization.
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 \& Data Architecture
+ Architect and lead end\-to\-end data transformation pipelines, including ingestion, normalization, quality, feature engineering, and real\-time processing
+ Design and implement Small Language Models (SLMs) optimized for domain\-specific tasks such as customer interactions, document understanding, or process automation
+ Build and maintain AI frameworks enabling scalable training, inference, experiment tracking, observability, and model governance
+ Architect and deploy AI Agents capable of orchestration, reasoning, planning, and workflow automation across enterprise systems
+ Develop and operationalize AI/ML models including:
+ Fraud detection (transaction scoring, anomaly detection)
+ Lending underwriting (risk modeling, creditworthiness, scoring pipelines)
+ Personalization, recommendation, NLP, forecasting, and more
- Technical Leadership \& Hands\-On Engineering
+ Lead technical design sessions, conduct POCs, and build reference architectures and reusable frameworks
+ Write high\-quality, production\-grade code in languages such as Python, Java, Scala, or TypeScript
+ Partners with data engineering teams to optimize Datalake/Lakehouse house, distributed computing, and feature engineering architectures
+ Evaluate emerging AI/ML technologies and drive adoption of best\-fit solutions
- AI\-Driven Developer Productivity
+ Leverage Claude Code, GitHub Copilot, and similar AI\-assisted development tools to:
+ Increase developer velocity
+ Improve code quality and consistency
+ Automate documentation, testing, and code review workflows
+ Reduce time\-to\-market for complex transformation initiatives
+ Evangelize AI\-powered engineering patterns and train teams on effective usage
- Application Modernization \& Transformation
+ Guide modernization of legacy systems into cloud\-native, AI\-native applications
+ Define patterns for integrating AI agents, LLMs, and SLMs into operational applications and microservices
+ Collaborate with platform teams to ensure efficient infrastructure, MLOps, CI/CD, and model deployment workflows
- Governance, Risk \& Compliance
+ Ensure responsible AI practices including fairness, explainability, model monitoring, ethics, and regulatory alignment (eg, lending compliance)
+ Implement robust evaluation and drift detection frameworks
- Cross\-Functional Collaboration
- Influence senior leaders, product teams, and engineering stakeholders on AI strategy and roadmap
- Act as technical mentor and thought leader across AI and data domains
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:
- 8\+ years in software engineering, data engineering, or machine learning
- 3\+ years designing large\-scale data transformation or AI architectures
- Hands\-on experience with:
+ Development skills in Java, Net, SQL Server / Oracle, Databrick, Power BI
+ AI/ML frameworks: PyTorch, TensorFlow, Scikit\-learn
+ LLMs \& SLMs: Llama models, Claude, custom SLM training
+ Data \& compute: Spark, Databricks, Flink, Kafka, Datalake/Lakehouse architecture
+ Cloud platforms: Azure, AWS, or GCP
- Experience building or integrating:
+ AI Agents
+ Vector databases (FAISS, Pinecone, Chroma)
+ Retrieval\-Augmented Generation (RAG) systems
- Proven solid skills in Python, SQL, and distributed systems
Preferred Qualifications:
- Experience building enterprise\-grade AI platforms or ML Ops frameworks
- Expertise in fraud, risk, underwriting, or financial modeling
- Hands\-on experience with AI coding tools (Claude Code, Copilot, Codex)
- Architectural leadership in cloud migration or application modernization
Soft Skills:
- Solid communication and storytelling, capable of influencing architecture decisions
- Ability to simplify complex concepts for non\-technical stakeholders
- Execution\-driven, collaborative mindset with a focus on business outcomes
- 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 $112,700 to $193,200 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 $112K-$193K 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($152K) sits 8% below the category median. Disclosed range: $112K to $193K.
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.
Frequently Asked Questions
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