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About This Role
Our Purpose
*Mastercard powers economies and empowers people in 200\+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.*
Title and Summary
Vice President, Product \- AI Center of Excellence
Role Overview
We are seeking a Vice President, AI Product Management to lead product strategy and execution for the company’s enterprise AI platform capabilities, including the Agent Factory control plane, agent development lifecycle, LLM enablement, evaluation frameworks, and governed AI build patterns.
This leader will own the product vision, roadmap, and operating model for enabling internal teams to design, build, evaluate, deploy, monitor, and scale trusted AI agents across the enterprise. The role will manage a small team of Product Managers and Product Managers\-Technical responsible for the platform capabilities that make AI agent development safe, reusable, observable, and production\-ready.
The ideal candidate has experience building enterprise platforms, developer platforms, AI/ML products, data platforms, or cloud\-native infrastructure products in a regulated or highly governed environment. They should be comfortable operating at the intersection of product strategy, technical architecture, data governance, risk management, and commercial value creation.
Key Responsibilities:
- Product Strategy \& Roadmap
o Define and own the multi\-year product strategy for the company’s Agent Factory and broader AI platform capabilities.
o Create a product roadmap that supports experimentation, agent build, certification, deployment, monitoring, and commercialization at scale.
o Partner with engineering, architecture, data, security, legal, compliance, risk, and business teams to align AI platform priorities to enterprise strategy.
o Translate emerging AI capabilities, including LLMs, agents, RAG, tool use, memory, evaluation harnesses, and multi\-agent orchestration, into practical enterprise product capabilities.
o Prioritize platform investments based on business value, reuse potential, technical feasibility, risk, cost, and customer adoption.
- Agent Factory Control Plane Ownership
o Own the product direction for the Agent Factory control plane, including:
Agent registry
Agent lifecycle management
Intake and approval workflows
Risk tiering
Model gateway
Prompt registry
Tool registry
Evaluation and certification workflows
Agent deployment governance
Observability and audit evidence
FinOps and value measurement
o Ensure the control plane provides a consistent, governed path from agent idea to production deployment.
o Partner with engineering to ensure the control plane integrates with AWS, Databricks, PCF/on\-prem environments, enterprise identity, access controls, data governance, and observability systems.
- AI Build \& Agent Enablement
o Lead the product strategy for AI build capabilities that enable teams to create high\-quality agents and LLM\-powered applications.
o Support platform capabilities such as:
Agent Studio / builder experience
Prompt playgrounds
Agent templates
LLM model access
RAG patterns
Tool\-calling frameworks
Agent evaluation harnesses
Guardrails
Human\-in\-the\-loop workflows
Runtime deployment patterns
Reusable agent components
o Establish standard patterns for building agents across data discovery, customer support, engineering productivity, fraud/risk, sales enablement, finance, operations, and internal knowledge workflows.
o Ensure product teams can safely experiment with agents in sandbox environments while maintaining a clear path to certified production deployment.
- Governance, Risk \& Compliance
o Embed responsible AI, data governance, privacy, security, and compliance requirements into the product lifecycle.
o Partner with legal, privacy, security, compliance, risk, and audit teams to define practical controls for production AI agents.
o Ensure every production agent has a named owner, risk classification, approved data sources, approved tools, evaluation evidence, telemetry, and a support model.
o Define approval gates and production\-readiness criteria based on agent autonomy, data sensitivity, tool access, regulatory exposure, and business impact.
o Drive consistency across AI governance, model governance, data governance, and enterprise access\-control policies.
- Customer Adoption \& Commercialization
o Drive adoption of the Agent Factory across internal product, engineering, data, and business teams.
o Create product experiences, onboarding guides, documentation, templates, and enablement programs that make it easier for teams to build agents through the platform.
o Develop metrics to track usage, adoption, reuse, quality, cost, risk, and business value.
o Support the evolution of the Agent Factory from internal platform capability to commercializable AI product foundation.
o Partner with business and product teams to identify AI agent capabilities that can be embedded into customer\-facing products, partner solutions, or monetizable services.
Required Qualifications
- Experience owning enterprise\-scale platforms, internal developer platforms, AI/ML platforms, data platforms, cloud platforms, workflow platforms, or governed technology products.
- Strong understanding of generative AI, LLMs, AI agents, RAG, prompt management, model evaluation, tool use, and enterprise AI risk considerations.
- Experience working in regulated, security\-conscious, or highly governed environments such as financial services, payments, banking, insurance, healthcare, or large enterprise technology.
- Ability to translate complex technical capabilities into clear product strategy, roadmaps, executive narratives, and customer\-facing value propositions.
- Strong executive communication skills with the ability to influence senior leaders across product, engineering, data, security, risk, compliance, legal, and business functions.
- Experience defining product metrics, OKRs, adoption goals, platform KPIs, and business value measures.
- Strong technical fluency with cloud, APIs, platform architecture, data governance, identity/access management, observability, and CI/CD concepts.
Mastercard is a merit\-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable\[email protected] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard’s security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job\-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro\-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best\-in\-class company match; deferred compensation for eligible roles; fitness reimbursement or on\-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on\-site fitness facilities in some locations.Pay Ranges
New York City, New York: $245,000 \- $391,000 USD
Salary Context
This $245K-$391K 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 Mastercard, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($318K) sits 45% above the category median. Disclosed range: $245K to $391K.
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.
Mastercard AI Hiring
Mastercard has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Harrison, NY, US, New York, NY, US, Arlington, VA, US. Compensation range: $266K - $391K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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