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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.
The VP, AI Transformation will lead the design and delivery of enterprise AI transformation programs across Finance, LCRA, Marketing, People, Government Affairs, and other corporate functions. The initial priority will be Finance, partnering closely with the CFO organization to modernize core processes, data, platforms, and ways of working through AI, automation, and digital technology.
This is a leadership role in our technology organization for someone who has successfully partnered with Finance (or other corporate function) executives and teams to deliver large\-scale transformation. The ideal candidate has led technology, data, AI, or digital product organizations and understands how Finance operates across areas such as FP\&A, controllership, accounting, treasury, tax, procurement, and financial reporting.
You will own the technology strategy, transformation portfolio, and delivery model for corporate functions. You will work at the intersection of business leadership, enterprise technology, data, engineering, cybersecurity, risk, and external partners. You will be accountable not only for deploying AI solutions, but also for establishing the architecture, data foundations, governance, reusable platforms, and internal capabilities required to scale them safely and economically.
Success will be measured by business outcomes: improvements in productivity, decision quality, forecast accuracy, control effectiveness, employee experience, speed, and cost\-not by the number of pilots or technologies deployed.
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:
Lead Finance AI and Technology Transformation
- Serve as the senior technology partner to the CFO and Finance leadership team
- Develop and own a multi\-year AI and technology transformation roadmap for Finance, aligned with Finance strategy, enterprise architecture, and business priorities
- Identify and prioritize high\-value opportunities across FP\&A, controllership, accounting operations, treasury, tax, procurement, financial reporting, and Finance shared services
- Modernize Finance workflows by combining AI, intelligent automation, data products, enterprise platforms, and process redesign
- Lead initiatives such as automated close and reconciliation, intelligent forecasting and scenario planning, management reporting, spend analytics, working\-capital optimization, financial controls, and self\-service decision support
- Ensure AI solutions integrate effectively with Finance platforms, data environments, and systems of record, including ERP, EPM, planning, reporting, procurement, and workflow platforms
- Partner with Finance, Internal Audit, Risk, Legal, Security, and Compliance to ensure solutions meet financial\-control, regulatory, privacy, security, and auditability requirements
Build and Scale the Enterprise Transformation Portfolio
- Own the portfolio of AI and technology transformation engagements across Finance, LCRA, Marketing, People, Government Affairs, and other corporate functions
- Establish Finance as the initial transformation domain, then apply successful delivery patterns, platform capabilities, and governance models to additional functions
- Translate functional strategies and operating challenges into a prioritized portfolio of technology products and transformation programs
- Determine which functions and use cases receive dedicated delivery teams based on value, feasibility, data readiness, risk, and strategic importance
- Maintain an enterprise backlog and make transparent investment, sequencing, scaling, and stop decisions
- Ensure every initiative has a clear business owner, technology owner, value case, adoption plan, and measurable outcome
Own Technology Strategy and Architecture
- Define the target technology architecture for enterprise AI transformation in partnership with enterprise architecture, data, cloud, integration, security, and infrastructure leaders
- Establish reusable technology patterns for generative AI, machine learning, intelligent automation, workflow orchestration, APIs, enterprise search, retrieval\-augmented generation, and AI agents
- Ensure solutions are built on secure, scalable, supportable enterprise platforms rather than disconnected proofs of concept
- Make build, buy, partner, and reuse decisions based on strategic differentiation, total cost of ownership, speed, risk, and long\-term maintainability
- Partner with ERP, EPM, data\-platform, and corporate\-systems leaders to embed AI capabilities into existing workflows and platforms
- Drive interoperability and avoid unnecessary duplication across functions, vendors, models, and data products
- Establish technical standards for solution design, integration, testing, observability, resiliency, model performance, and production support
Strengthen Data, Governance, and Controls
- Secure the data access, integration, governance, and quality pathways required to deliver transformation at enterprise scale
- Partner with data owners and technology teams to establish trusted, governed Finance data products for AI, analytics, reporting, and automation
- Ensure appropriate controls for data lineage, access, privacy, retention, segregation of duties, financial reporting, and model use
- Establish risk\-tiering and governance processes that allow lower\-risk use cases to move quickly while applying appropriate oversight to higher\-risk applications
- Ensure AI outputs are explainable, traceable, monitored, and auditable where required
- Work with cybersecurity, privacy, legal, compliance, and enterprise\-risk teams to operationalize responsible AI standards throughout the delivery lifecycle
Lead Technology Delivery and Product Management
- Establish a product\-oriented operating model that brings together business product owners, product managers, architects, engineers, data scientists, designers, change leaders, and functional subject\-matter experts
- Lead multidisciplinary delivery teams responsible for taking opportunities from discovery through architecture, build, deployment, adoption, and ongoing optimization
- Set the engineering and product\-management expectations for quality, security, reuse, documentation, and production readiness
- Implement disciplined portfolio, product, and agile delivery practices while maintaining appropriate controls for enterprise technology programs
- Hold teams accountable for measurable adoption and realized value, not simply technical deployment
- Ensure solutions transition into sustainable ownership, support, and lifecycle\-management models
Build a Reusable Enterprise AI Capability
- Steward the flywheel that turns individual use\-case learnings into reusable platform services, data products, architecture patterns, governance controls, and delivery accelerators
- Hold the organization accountable for reducing the marginal cost and time required to deliver each additional use case or functional transformation
- Build common capabilities for model access, prompt and agent management, knowledge retrieval, evaluation, monitoring, human review, security, and workflow integration
- Create mechanisms for sharing technology assets and delivery patterns across Finance and other corporate functions
- Establish clear criteria for moving solutions from experimentation to production and from function\-specific implementations to enterprise services
Develop the Organization and Partner Ecosystem
- Build and lead a senior organization spanning technology strategy, product management, architecture, engineering, data, AI delivery, and transformation leadership
- Set a high bar for hiring and talent\-development for both technical leaders and individual contributors
- Develop solid relationships with Finance leaders, enterprise technology teams, and functional executives
- Manage the transition from partner\- or consultancy\-led delivery to a durable internal technology capability
- Select and manage strategic technology vendors, systems integrators, AI platform providers, and specialist partners
- Ensure external partners transfer knowledge, use enterprise standards, and contribute reusable assets rather than creating long\-term dependency
- Establish workforce and sourcing plans that balance speed, specialized expertise, intellectual\-property ownership, and operating cost
Measure and Communicate Value
- Define and maintain the business case for the transformation portfolio, including technology investment, expected value, delivery risk, adoption, and ongoing operating cost
- Report portfolio performance, architecture decisions, risks, dependencies, and value realization to executive leadership
- Establish metrics for productivity, cycle time, cost, quality, forecast accuracy, control effectiveness, adoption, customer experience, and employee experience
- Make evidence\-based recommendations about which solutions to scale, redesign, consolidate, or stop
- Ensure benefits are validated with Finance and other functional leaders and can be defended through transparent measurement
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:
- 15\+ years of experience in technology, engineering, data, product, enterprise applications, or digital transformation leadership
- Several years of experience leading other technology leaders, multidisciplinary teams, or a significant enterprise technology organization
- Demonstrated success serving as a technology leader or strategic technology partner to Finance and CFO organizations
- Experience delivering technology transformation across one or more Finance domains, such as FP\&A, controllership, accounting, treasury, tax, procurement, financial reporting, or shared services
- Track record of leading enterprise AI, data, automation, ERP, EPM, or digital\-platform programs with direct accountability for measurable business outcomes
- Experience translating Finance and business requirements into technology strategy, architecture, product roadmaps, and delivery plans
- Solid understanding of enterprise architecture, cloud platforms, data platforms, integration patterns, cybersecurity, identity, and software delivery
- Solid working knowledge of modern AI capabilities, including generative AI, large language models, AI agents, machine learning, retrieval\-augmented generation, and intelligent automation
- Experience moving AI or digital products from experimentation into secure, governed, production\-scale operations
- Demonstrated ability to navigate enterprise data access, data quality, governance, privacy, risk, and control requirements
- Experience evaluating build\-versus\-buy decisions and managing enterprise technology vendors and implementation partners
- Credibility with CFOs and Finance leaders, as well as CIOs, architects, engineers, data scientists, security leaders, and risk professionals
- Ability to communicate complex technology decisions clearly to senior executives and boards or executive committees
The strongest candidates will have experience across several of the following areas:
- Enterprise Finance platforms, including ERP, EPM, planning, consolidation, reporting, procurement, treasury, tax, and financial\-close technologies
- Modern cloud and data architectures, including data lakes or lakehouses, data warehouses, APIs, integration platforms, master data, metadata, and data governance
- Generative AI platforms and patterns, including LLM gateways, RAG, enterprise search, agents, orchestration, evaluation, monitoring, and human\-in\-the\-loop controls
- Machine learning, analytics, business intelligence, process mining, workflow, robotic process automation, and intelligent document processing
- Secure software engineering, DevSecOps, MLOps, LLMOps, testing, observability, reliability, and production\-support practices
- AI governance, model risk, privacy, cybersecurity, responsible AI, financial controls, and regulatory compliance
- Product operating models, portfolio management, agile delivery, OKRs, value realization, and technology\-finance management
Preferred Qualifications:
- Experience leading Finance technology, corporate systems, enterprise applications, data and analytics, or AI within a large global enterprise
- Experience working in a regulated industry such as healthcare, financial services, insurance, or life sciences
- Experience with large\-scale ERP or Finance\-platform modernization
- Experience establishing or scaling an AI engineering, data\-product, forward\-deployed engineering, solutions\-engineering, or internal\-platform organization
- Experience creating reusable enterprise AI services and reducing the cost and delivery time of subsequent use cases
- Experience managing a transition from consultancy\-led programs to internally owned technology products and capabilities
- Familiarity with change management, operating\-model redesign, and adoption programs for Finance and other corporate functions
- Advanced degree in computer science, engineering, information systems, business, finance, or a related field
- 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 $200,400 to $343,500 annually based on full\-time employment. We comply with all minimum wage laws as applicable.
*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 $200K-$343K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($271K) sits 27% above the category median. Disclosed range: $200K to $343K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Optum AI Hiring
Optum has 18 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Minnetonka, MN, US, Eden Prairie, MN, US, Brentwood, TN, US. Compensation range: $176K - $348K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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