Interested in this AI/ML Engineer role at Genworth?
Apply Now →About This Role
At Genworth, we empower families to navigate the aging journey with confidence. We are compassionate, experienced allies for those navigating care with guidance, products, and services that meet families where they are. Further, we are the spouses, children, siblings, friends, and neighbors of those that need care—and we bring those experiences with us to work in serving our millions of policyholders each day.
We apply that same compassion and empathy as we work with each other and our local communities. Genworth values all perspectives, characteristics, and experiences so that employees can bring their full, authentic selves to work to help each other and our company succeed. We celebrate our diversity and understand that being intentional about inclusion is the only way to create a sense of belonging for all associates. We also invest in the vitality of our local communities through grants from the Genworth Foundation, event sponsorships, and employee volunteerism.
Our four values guide our strategy, our decisions, and our interactions:
- Make it human. We care about the people that make up our customers, colleagues, and communities.
- Make it about others. We do what's best for our customers and collaborate to drive progress.
- Make it happen. We work with intention toward a common purpose and forge ways forward together.
Make it better. We create fulfilling purpose\-driven careers by learning from the world and each other.
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POSITION TITLE
Vice President, Enterprise Architecture and AI
This role is not eligible for employment visa sponsorship.
POSITION LOCATION
This position is available to Virginia residents in Richmond, Virginia in‑office/hybrid applicants.
- A Hybrid schedule of both Remote and In\-Office days is required. In office days are Tuesday, Wednesday and Thursday with working hours targeting our core business hours of 9am\-5pm EST.
YOUR ROLE
Genworth \| CareScout 's Technology Organization is on a transformational journey to become a leading enterprise technology function, one that drives innovation, enables business agility, and delivers scalable, resilient, and customer\-centric solutions. At this pivotal moment, Genworth \| CareScout is seeking a visionary and transformational executive to serve as Vice President, Enterprise Architecture and AI, reporting directly to the Chief Information Officer.
This Vice President will own the enterprise\-wide architecture vision and AI strategy that shapes how we build, modernize, and evolve our technology landscape. The role carries full executive accountability for defining technology direction, enterprise standards, governance frameworks, and reference architectures, while simultaneously leading AI as a transformative enterprise capability that accelerates business outcomes across Genworth and CareScout.
As a critical member of the Technology Leadership Team, this role will set the architectural and AI agenda for the enterprise, chair the Architecture Review Board, and serve as a key voice in shaping Genworth's goals and Future\-Proof Technology Strategy. The VP oversees the full Architecture discipline: Enterprise Architecture, Data Architecture, Infrastructure Architecture, Platform Architecture, Security Architecture, Solution Architecture, and the Architecture leadership function—as well as enterprise AI strategy, governance, and enablement.
This is a unique opportunity to shape the future of technology at Genworth \| CareScout, lead cross\-functional teams, and drive enterprise\-wide transformation across a diverse portfolio of insurance, digital, and services businesses.
WHAT YOU WILL BE DOING
Enterprise Architecture Strategy \& Vision
- Set and own Genworth's enterprise architecture vision and long\-term technology strategy, ensuring direct alignment with business objectives and the Future\-Proof Technology Strategy
- Establish and govern target\-state architectures, business capability models, value streams, and enterprise technology roadmaps that drive investment decisions, platform modernization, and portfolio rationalization
- Lead enterprise\-wide current\-state assessments and strategic gap analyses across application, data, infrastructure, security, and integration domains to shape transformation priorities and capital allocation
- Drive architectural coherence and technology simplification across the enterprise, reducing fragmentation and ensuring solutions are scalable, interoperable, and aligned to shared enterprise standards
- Serve as the enterprise's chief architectural authority, providing executive\-level strategic counsel on technology direction, platform strategy, emerging risks, and long\-term technology investments
AI Strategy \& Enterprise Enablement
- Own and drive the enterprise AI strategy, architecture roadmap, and reference frameworks that enable scalable, responsible, and measurable AI adoption across Genworth and CareScout
- Lead the vision and architecture for AI capabilities including machine learning, generative AI/LLMs, intelligent automation, agentic AI patterns, and advanced analytics at enterprise scale
- Establish comprehensive AI governance frameworks covering model lifecycle management, responsible AI principles, data quality, bias monitoring, explainability, and regulatory compliance
- Own the enterprise AI intake and prioritization process, ensuring AI investments are strategically aligned to high\-value business outcomes and governed by enterprise architecture standards
- Partner with executive leaders across the organization to identify transformative AI opportunities and drive enterprise\-wide adoption
- Architect enterprise AI platforms and integration patterns that ensure scalability, reproducibility, observability, cost efficiency, and security across production environments
- Represent the company’s AI vision and capabilities to the Board of Directors, regulators, industry forums, and strategic partners
Architecture Governance \& Standards
- Establish and chair the Architecture Review Board (ARB), ensuring enterprise architectural decisions are consistent, strategically governed, and positioned to enable—not gate—delivery
- Define and enforce architecture governance processes, policies, and enterprise standards leveraging frameworks such as TOGAF and industry best practices
- Own the development and evolution of architecture principles, reference models, technology standards, and enterprise pattern libraries that accelerate delivery and promote reuse at scale
- Govern the enterprise technology portfolio to optimize investments, reduce redundancy, retire technical debt, and ensure alignment to target\-state architectures and business priorities
- Ensure enterprise architecture and AI practices support regulatory compliance, data governance, privacy, and security\-by\-design principles across all technology domains
Business Partnership \& Strategic Advisory
- Serve as a trusted executive advisor to the CIO, executive leadership team, and Board of Directors on technology direction, AI strategy, architectural trade\-offs, and enterprise risk
- Translate complex business and market challenges into elegant, scalable architectural strategies that balance immediate operational needs with long\-term enterprise positioning
- Partner at the executive level with Engineering, Cybersecurity, Infrastructure, Delivery, and business leaders to ensure architectural alignment and shared accountability across all technology domains
- Own enterprise\-level build vs. buy recommendations, vendor platform evaluations, and strategic technology investment decisions with clear architectural and business rationale
- Deliver executive\-level reporting on architecture maturity, AI enablement progress, technology risk posture, and strategic outcomes to senior leadership and Board\-level governance forums
- Represent Genworth externally in industry forums, technology partnerships, and peer networks to advance the company's technology reputation and inform strategic direction
Technology Modernization \& Innovation
- Drive enterprise technology modernization strategy including cloud adoption, API\-enabled architectures, microservices, platform rationalization, and legacy\-to\-modern transitions across Genworth's insurance and digital ecosystems
- Own enterprise integration strategy connecting core insurance platforms, digital channels, data ecosystems, CareScout services, and third\-party partners through modern, scalable architectural patterns
- Anticipate and evaluate emerging technologies, architectural paradigms, and AI advancements, providing the enterprise with a forward\-looking perspective on opportunities, risks, and strategic implications
- Champion innovation by sponsoring proof\-of\-value initiatives across AI, automation, intelligent document processing, advanced analytics, and next\-generation platform capabilities
- Advance business target architectures and the enterprise architecture pattern library to accelerate delivery velocity and promote consistent, reusable solutions across the organization
Build \& Lead High\-Performing Teams
- Build, lead, and scale a world\-class architecture and AI organization, attracting and retaining top talent across enterprise architecture, solution architecture, data architecture, AI engineering, and related disciplines
- Develop and mentor the next generation of architecture and AI leaders, building deep bench strength and a clear succession pipeline
- Foster a community of practice across architecture roles—unifying Principal Architects, Solution Architects, Business Architects, and Technology Architects under a shared vision, common framework, and collaborative culture
- Lead through transformation with executive presence, empathy, and clarity, helping teams and the broader organization navigate change and the rapidly evolving AI landscape
Champion a culture of innovation, accountability, continuous learning, and business\-aligned decision\-making that reflects Genworth's values: Make it human, Make it about others, Make it happen, Make it better
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WHAT YOU BRING
- 15\+ years of progressive experience in enterprise architecture, AI/ML strategy, or enterprise technology leadership, with 10\+ years in senior leadership roles with accountability for enterprise\-wide outcomes
- Proven expertise in enterprise architecture frameworks (TOGAF, Zachman) with a demonstrated track record of defining, governing, and evolving architecture strategy at the enterprise level
- Deep understanding of AI/ML platforms, generative AI/LLM architectures, responsible AI governance, and operationalizing AI at enterprise scale in complex, regulated environments
- Demonstrated success leading large\-scale enterprise technology transformation—cloud migration, platform modernization, legacy\-to\-modern transitions—in similarly complex organizations
- Strong executive presence with a proven track record presenting to and influencing C\-suite, Board of Directors, and regulatory audiences on technology strategy and risk
- Deep and broad technical knowledge spanning applications, data, infrastructure, cloud platforms, security, integration, and modern software engineering practices
- Proven ability to build, lead, and develop large, multi\-disciplinary architecture and AI organizations with a focus on talent development, succession planning, and high\-performance culture
- Demonstrated ability to operate effectively in matrixed organizations, influencing without authority and driving outcomes across complex stakeholder landscapes
- Experience managing large technology budgets, capital planning, and vendor relationships with clear financial stewardship
Exceptional communication, collaboration, and executive presentation skills with the ability to simplify complexity and drive alignment across diverse audiences
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NICE TO HAVE
- Insurance or financial services industry experience, particularly in life insurance, long\-term care, annuities, or property \& casualty
- Experience with insurance business processes, regulatory frameworks (state insurance departments, NAIC, SOX, privacy regulations), and enterprise risk management
- Background in enterprise AI platform engineering, MLOps, and production\-grade AI system design at scale
- MBA, advanced degree in Computer Science, Engineering, or related field
- Experience with SAFe, Agile, or product\-oriented delivery models in architecture\-governed environments
- TOGAF certification or equivalent enterprise architecture professional credential
- Familiarity with ServiceNow, CMDB, and IT portfolio management tools
- Experience with aging services, healthcare\-related technology, or direct\-to\-consumer digital platforms
- Published thought leadership, industry conference presentations, or recognized contributions to the enterprise architecture or AI communities
ADDITIONAL INFORMATION
National Range: $204,400 \- $317,000
Disclaimer: This role will be located in Richmond, VA. Actual compensation will vary based on geographic location, experience, skills, and other job\-related factors. In addition to base salary, this role is eligible to participate in a bonus incentive plan. Incentive compensation is based on individual and company performance and is not guaranteed.
Employee Benefits \& Well\-Being
Genworth employees make a difference in people’s lives every day. We’re committed to making difference in our employees’ lives.
- Competitive Compensation \& Total Rewards Incentives
- Comprehensive Healthcare Coverage
- Multiple 401(k) Savings Plan Options
- Auto Enrollment in Employer\-Directed Retirement Account Feature (100% employer\-funded!)
- Generous Paid Time Off – Including 12 Paid Holidays, Volunteer Time Off and Paid Family Leave
- Disability, Life, and Long Term Care Insurance
- Tuition Reimbursement, Student Loan Repayment and Training \& Certification Support
- Wellness support including gym membership reimbursement and Employee Assistance Program resources (work/life support, financial \& legal management)
- Caregiver and Mental Health Support Services
Salary Context
This $204K-$317K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Genworth, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($260K) sits 19% above the category median. Disclosed range: $204K to $317K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Genworth AI Hiring
Genworth has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $234K - $317K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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