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Job Classification:
Technology \- Agile, Delivery, \& Product
Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability, and efficiency? The Global Technology team takes great pride in our culture, where digital transformation is built into our DNA. When you join Prudential, you’ll unlock an exciting and impactful career — while growing your skills and advancing your profession at one of the world’s leading financial services institutions.
Your Team \& Role
As the Vice President, Data \& AI Engineering, reporting to the Chief Data \& AI Officer, you will lead Prudential’s forward deployed data and AI engineering organizations responsible for building, scaling, and operationalizing trusted, AIready data products across the enterprise.Your organization functions as Prudential’s front‑line delivery engine for data and AI, deploying elite engineering teams to the company’s highest‑priority business initiatives. Rather than permanently embedding within a single domain, these teams rotate across business areas — accelerating delivery, compounding institutional knowledge, and ensuring consistent engineering standards across the enterprise.
Your scope includes Data Engineering, AI Engineering, Machine Learning Engineering (including MLOps and LLMOps), Agentic Engineering, Conversational AI, Intelligent Automation (including RPA and workflow automation), BI \& Analytics Engineering, DataOps, and Data Product Certification. You are accountable for translating advanced analytics and AI capabilities into measurable business outcomes, while balancing speed and innovation with enterprise reliability, security, and responsible AI.
In this role, you will partner closely with senior business leaders, product teams, architecture, governance, risk, legal and compliance, security, workforce, and platform leaders to ensure data and AI investments deliver sustained enterprise value. This role requires deep technical judgment, strong delivery leadership, and the ability to mobilize highly skilled engineering teams against Prudential’s most complex, high‑impact AI‑driven opportunities.
What You Can Expect on a Typical Day
- Lead and scale forward‑deployed data and AI engineering acceleration teams that rotate across business domains to deliver high‑impact, production‑ready solutions aligned to enterprise priorities.
- Own the end‑to‑end engineering execution of data, AI, and machine learning solutions — from domain onboarding and data sourcing through certification, production deployment, and operationalization.
- Serve as the senior engineering leader accountable for delivery quality, technical rigor, and production reliability across data engineering, AI engineering, ML engineering, agentic systems, BI \& analytics, and intelligent automation.
- Deploy specialized engineering teams to complex, ambiguous business problems, rapidly designing and implementing solutions that reduce time‑to‑value and deliver measurable outcomes.
- Oversee the certification and operationalization of Authorized Data Sources and AI‑ready data products, ensuring data quality, lineage, access control, audit readiness, and agent‑readiness standards are met.
- Establish and reinforce AI‑assisted engineering practices, embedding AI tooling into the development lifecycle to materially increase engineering productivity and velocity.
- Partner closely with business leaders and transformation sponsors to translate strategic priorities into executable engineering roadmaps and delivery plans.
- Collaborate with platform, architecture, security, and governance leaders to ensure solutions are engineered in alignment with enterprise standards while maintaining delivery speed.
- Ensure AI and data solutions are built with operational readiness in mind, including monitoring, observability, incident management, and post‑deployment support as teams rotate to new domains.
- Provide hands‑on technical leadership and act as an escalation point for complex engineering decisions, delivery risks, and design tradeoffs.
- Communicate engineering progress, risks, and outcomes clearly to executive stakeholders, enabling informed prioritization and investment decisions.
- Build and sustain a high‑performance, inclusive engineering culture focused on craftsmanship, continuous learning, accountability, and mission‑driven execution.
The Skills \& Expertise You Bring
- Bachelor’s degree in computer science, engineering, or a related field; advanced degree preferred.
- Extensive experience leading large‑scale data and AI engineering organizations in complex, enterprise environments.
- Deep, hands‑on expertise across data engineering, AI engineering, machine learning engineering, and analytics, with a strong understanding of how these capabilities come together in production.
- Proven track record of delivering production‑grade data and AI solutions that drive real business outcomes — not just pilots or proofs of concept.
- Strong technical fluency in modern data and AI stacks, including data pipelines, ML and LLM lifecycle management, model deployment, feature engineering, and AI‑enabled solution design.
- Demonstrated ability to deploy and lead elite engineering teams against high‑stakes, high‑visibility enterprise initiatives.
- Experience operating in a federated enterprise environment, partnering effectively with platform, architecture, security, and governance teams while retaining accountability for delivery.
- Strong business acumen, with the ability to translate ambiguous business problems into clear, executable engineering solutions.
- Exceptional communication and storytelling skills, with the ability to explain complex technical concepts to executive and non‑technical audiences.
- Proven success building engineering leaders, developing deep technical talent, and scaling organizations without losing velocity or quality.
- Comfort operating in ambiguity, making sound technical decisions under pressure, and driving execution in fast‑moving, highly visible environments.
What we offer you:
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- 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 Programsincluding 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), after one year of service.
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.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Prudential AI Hiring
Prudential has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety. Based in Newark, NJ, US. Compensation range: $267K - $359K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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