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Job Classification:
Technology - Engineering & CloudPGIM is seeking a strategic and execution-focused Director, AI Technical Lead to lead the end-to-end delivery of enterprise AI initiatives across PGIM Technology and business partners.
This role will focus on identifying, prioritizing, and scaling high-value AI use cases, ensuring alignment to business outcomes, and driving measurable impact through disciplined program governance and execution.
The ideal candidate blends business acumen, AI fluency, and strong program leadership to translate opportunities into scalable solutions.
What You Will Do:
AI Strategy Execution & Use Case Development
- Partner with business leaders (investments, operations, distribution, risk) to identify and prioritize AI use cases aligned to strategic objectives
- Translate business problems into clear AI opportunities, including value hypotheses, feasibility, and implementation roadmaps
- Build and maintain an enterprise AI use case pipeline, including prioritization based on ROI, complexity, and scalability
- Define success criteria, KPIs, and business outcomes for each initiative
End-to-End AI Program Management
- Lead the delivery of AI programs from concept to production, including pilots, scaling, and adoption
- Establish and run program governance, including steering committees, status reporting, and risk/issue management
- Coordinate across data, engineering, architecture, cyber, legal/compliance, and business teams to ensure successful execution
- Manage dependencies across concurrent AI initiatives and ensure alignment to enterprise technology strategy
Business Engagement & Value Realization
- Act as the primary interface between business stakeholders and technology teams for AI initiatives
- Ensure solutions are business-led and outcome-driven, not technology-led experiments
- Track and report realized value (cost savings, productivity gains, revenue enablement, risk reduction)
- Drive adoption and integration into business workflows to ensure sustained impact
AI Productization & Scaling
- Transition successful pilots into scaled, production-grade AI solutions
- Partner with architecture and engineering to ensure solutions are secure, compliant, reusable, and integrated into enterprise platforms
- Promote standardization through reusable components, patterns, and AI capabilities (e.g., copilots, knowledge assistants, automation frameworks)
Governance, Risk & Responsible AI
- Ensure all AI initiatives adhere to enterprise governance, model risk management, and regulatory expectations
- Partner with Risk, Legal, and Compliance teams to implement responsible AI practices
- Establish controls for data usage, model transparency, and monitoring
Executive Reporting & Communication
- Provide clear, concise executive reporting on AI portfolio progress, risks, and outcomes
- Develop materials for senior leadership, including CTO, business heads, and executive committees
- Highlight key decisions, trade-offs, and investment priorities
What You Will Bring
- 10+ years of experience in program management, technology delivery, or strategic initiatives, with a strong focus on AI, data, or digital transformation
- Proven experience delivering cross-functional enterprise programs with measurable business outcomes
- Strong understanding of AI/ML concepts, generative AI, and data platforms (not hands-on modeling required, but fluency expected)
- Experience working in or with asset management, financial services, or regulated industries
- Demonstrated ability to translate business needs into technology solutions
- Strong stakeholder management skills with experience engaging senior executives
- Structured, disciplined approach to governance, prioritization, and execution
- Ability to operate in a matrixed, global organization
What Sets This Role Apart
- Direct impact on PGIM’s enterprise AI agenda and business transformation
- Ownership of a high-visibility portfolio of AI initiatives tied to measurable outcomes
- Opportunity to shape how AI is embedded into investment, operations, and client workflows
What we offer you:
- 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.
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. Director-level AI roles across all categories have a median of $272,300.
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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