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Job Classification:
Corporate - People Team As the Vice President, AI Workforce Transformation Leader, you will serve as the architect for an AI‑enabled work redesign. In this newly created, high‑impact role, you will help shape the future of how Prudential works by driving the cultural, workforce, and organizational shifts required to fully realize the value of AI. You will help define the vision, frameworks, and operating models that enable human‑AI teaming, accelerate adoption, and position Prudential’s workforce for long‑term success in an AI‑augmented environment.
This role reports jointly to the Chief Talent & Capability Officer and the Chief Data and AI Officer. You will co‑own the AI workforce transformation strategy with the CDAIO, with direct access to technical roadmaps, platform adoption data, and engineering resources to inform your planning. Through this partnership, you will guide decisions on job transformation, talent strategy, and organizational change across every division and geography. Through scalable, human‑centered models, you will strengthen business agility, elevate productivity, and ensure Prudential is equipped to lead in a rapidly evolving digital landscape.
Strategic Context and Impact
This role exists at a pivotal moment for Prudential. The technology foundation is largely in place; what remains is the harder, more important work of transforming how our people work, how we develop talent, and how we lead through unprecedented change. You will operate within the following enterprise context:
- You will lead workforce transformation across a global footprint including the U.S., Japan, Brazil, Mexico, and Global Capability Centers in India and Ireland, each with distinct regulatory, cultural, and talent dynamics.
- Your workforce strategies will directly determine Prudential’s ability to capture value at stake for efficiencies across the workforce.
- The Enterprise Agentic AI Platform enters production in 2026, introducing autonomous AI agents that fundamentally change how work is supervised and how jobs are structured.
- Prudential has a $76.3M FY26 Data and AI portfolio investment, with 300+ AI use cases in flight across 18 business units and 75+ deployed AI solutions.
Here is What You Can Expect on a Typical Day
- Shape and champion the vision for AI‑enabled job and work redesign, ensuring leaders and teams are aligned and inspired to adopt new ways of working.
- Develop and evolve the organizational and work redesign roadmap, translating strategy into clear frameworks, priorities, and decision structures.
- Define the guiding principles for human–AI teaming, informing workforce planning, leadership expectations, and role evolution across the enterprise.
- Design the organizational model for human‑agent collaboration, addressing how teams function when some members are AI agents, how work is delegated between humans and autonomous systems, and what new governance structures are required.
- Lead the workforce strategy for agentic AI adoption, including defining new roles for agent supervision and quality governance, establishing accountability frameworks for autonomous AI decisions, and planning workforce transitions as AI agents assume multi‑step workflow execution.
- Design agile, modular organizational structures that promote cross-functional collaboration, rapid execution, and scalable adoption of AI‑driven work models.
- Maintain and refine a standardized AI delivery framework that supports consistent scaling, adaptation, and integration across businesses and geographies.
- Streamline processes and embed agile ways of working to improve execution speed, enhance learning cycles, and accelerate the deployment of AI solutions.
- Lead the creation and continuous improvement of global work and role redesign processes, including enterprise playbooks, toolkits, and localization guidelines.
- Build and nurture a collaborative ecosystem that enables knowledge sharing, cross-functional alignment, and efficient use of AI talent and resources.
- Oversee governance for AI‑enabled work redesign, integrating risk, ethics, privacy, compliance, and change management into all workforce‑related AI initiatives.
- Review and guide all talent‑ and work‑related AI initiatives, ensuring they meet enterprise standards and follow appropriate governance pathways.
- Represent the People organization on the AI Steering Committee, shaping decisions that guide enterprise transformation and people‑technology integration.
- Optimize the deployment and development of AI talent, ensuring skills are aligned to high‑value work and workforce plans support enterprise needs.
- Design and evolve technical career paths, competency frameworks, and talent development strategies that build and retain AI capabilities.
- Partner to deliver the Innovation Academy, cultivating enterprise fluency, experimentation, and shared learning around AI‑enabled work.
- Define and track KPIs that measure the value, productivity, adoption, and ROI of work redesign efforts.
- Ensure work redesign efforts drive measurable business impact, remain responsive to global needs, and evolve with new insights and emerging trends.
The Skills & Expertise You Bring
- Bachelor’s degree in Human Resources, Organizational Development, Business, or a related field; advanced degree preferred.
- Proven experience leading workforce strategy, organizational design, and large‑scale transformation in complex, matrixed environments, acting as a strategic architect who translates enterprise strategy into scalable, actionable work and organizational models.
- Demonstrated ability to design enterprise-wide strategies that connect people, technology, and business value while building ecosystems that enable transparency, alignment, and shared learning across functions.
- Expertise in job redesign, organizational model transformation, and human–AI teaming, with a future‑of‑work mindset that anticipates trends and advocates for adaptive change.
- Strong understanding of AI/ML concepts and their implications for roles, skills, leadership, and culture, with the ability to translate complex AI capabilities into practical workforce, process, and business impacts.
- Track record of leading high‑impact change, shaping culture, and guiding organizations through ambiguity with clarity, influence, and strong storytelling, driving adoption of new ways of working.
- Demonstrated ability to design and lead enterprise change management strategies that address employee anxiety, build psychological safety for experimentation, and sustain engagement through multi‑year transformation.
- Experience leading workforce transformation across multiple geographies with distinct regulatory environments, labor markets, and cultural contexts, including capability center strategy and integration.
- Experience developing talent architectures, career paths, competency models, and learning ecosystems that integrate people and technology to advance enterprise priorities.
- Strong grounding in governance, ethics, compliance, and responsible AI principles, with the ability to design talent and org strategies that balance innovation with disciplined risk management.
- Skilled in establishing KPIs, measuring business outcomes, and using data to ensure work redesign delivers meaningful value, productivity gains, engagement, and ROI.
What we offer you:
Prudential is required by state specific laws to include the salary range for this role when hiring a resident in applicable locations. The salary range for this role is from $205,600.00 to $308,300.00. Specific pricing for the role may vary within the above range based on many factors including geographic location, candidate experience, and skills.* Market competitive base salaries, with a yearly bonus potential at every level.
- Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave.
- 401(k) plan with company match (up to 4%).
- Company-funded pension plan.
- Wellness Programs including up to $1,600 a year for reimbursement of items purchased to support personal wellbeing needs.
- Work/Life Resources to help support topics such as parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development.
- Education Benefit to help finance traditional college enrollment toward obtaining an approved degree and many accredited certificate programs.
- Employee Stock Purchase Plan: Shares can be purchased at 85% of the lower of two prices (Beginning or End of the purchase period).
Eligibility to participate in a discretionary annual incentive program is subject to the rules governing the program, whereby an award, if any, depends on various factors including, without limitation, individual and organizational performance. To find out more about our Total Rewards package, visit Work Life Balance | Prudential Careers. Some of the above benefits may not apply to part-time employees scheduled to work less than 20 hours per week.
Prudential Financial, Inc. of the United States is not affiliated with Prudential plc. which is headquartered in the United Kingdom.
Prudential is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender identity, national origin, genetics, disability, marital status, age, veteran status, domestic partner status, medical condition or any other characteristic protected by law.
To maintain the integrity and security of our hiring process, Prudential may use identity verification technologies. Your consent will be obtained before any verification takes place.
If you need an accommodation to complete the application process, please email [email protected].
If you are experiencing a technical issue with your application or an assessment, please email [email protected] to request assistance.
Salary Context
This $205K-$308K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Prudential, 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($256K) sits 21% above the category median. Disclosed range: $205K to $308K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Prudential AI Hiring
Prudential has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Newark, NJ, US. Compensation range: $308K - $308K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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