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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
Director, Forward\-Deployed AI Engineer \- AI Mobilization \& Transformation
Overview
The Director, Forward\-Deployed AI Engineer serves as Mastercard's embedded AI transformation leader, partnering directly with business units to identify high\-value opportunities, develop production\-grade AI solutions, and mobilize teams to adopt new ways of working.
Reporting to the VP of Organizational Readiness, this role combines deep technical expertise with change leadership. Rather than building solutions in isolation, you will work alongside business teams to solve real problems, demonstrate the art of the possible, and develop internal capability through hands\-on engagement.
Success is measured not only by the solutions delivered, but by the number of leaders, engineers, analysts, and teams equipped to independently leverage AI, agents, and multi\-agent systems in their daily work.
The Role
Mobilizing AI Adoption Through Bespoke Engagements
- Embed within business units to identify strategic workflow, productivity, and decision\-making opportunities where AI can create measurable value.
- Lead AI Transformation Engagements that combine discovery, solution design, implementation, and capability building.
- Build high\-impact use cases that serve as showcase examples for broader organizational adoption.
- Translate business challenges into practical applications of AI, agents, and multi\-agent orchestration.
- Create reusable playbooks, patterns, and training assets that accelerate adoption across the enterprise.
- Partner with business leaders to demonstrate measurable outcomes and establish local AI champions.
- Develop repeatable transformation approaches that can be scaled across multiple business units and functions.
- Identify and prioritize high\-value opportunities that accelerate enterprise AI adoption and capability growth.
Building While Teaching
- Design and deploy production\-ready AI assistants, agents, and orchestration frameworks that solve real business problems.
- Use each engagement as a live learning environment where business and technical teams learn modern AI practices through delivery.
- Coach engineers, analysts, product managers, and operational teams on AI\-first ways of working.
- Establish a "train\-the\-trainer" model that enables local teams to continue scaling capabilities after engagements conclude.
- Facilitate hands\-on workshops focused on prompt engineering, agent design, workflow automation, Copilot practices, and AI\-assisted development.
- Develop internal champions capable of independently driving AI adoption and solution delivery.
- Promote knowledge sharing and adoption of best practices across teams and business units.
Advancing Agentic Transformation
- Architect and implement solutions leveraging Copilot Studio, Azure AI, agent frameworks, orchestration systems, and enterprise platforms.
- Develop multi\-agent solutions that automate complex business processes and decision flows.
- Introduce modern engineering practices including AI\-assisted software development, evaluation frameworks, observability, and governance.
- Establish proven reference architectures and patterns that can be replicated across business units.
- Help business teams evolve from experimentation to operationalized AI solutions.
- Partner with engineering and business leaders to drive scalable adoption of agentic solutions across the enterprise.
- Evaluate emerging AI capabilities and identify opportunities to apply them to business challenges.
Capturing and Scaling Organizational Learning
- Document emerging patterns, successful use cases, implementation approaches, and lessons learned.
- Build an enterprise library of AI\-enabled workflows, agents, and transformation stories.
- Identify adoption barriers and design interventions that accelerate organizational readiness.
- Contribute to enterprise readiness metrics by measuring adoption, productivity gains, capability growth, and business impact.
- Create a feedback loop between field engagements, engineering teams, and organizational readiness programs.
- Capture and share best practices, reusable assets, and implementation patterns across engagements.
- Drive continuous improvement of AI transformation approaches based on engagement outcomes and organizational learning.
All About You
- 5\+ years of software engineering experience with a track record of building and deploying production\-grade systems.
- Deep experience with AI technologies including LLMs, agent frameworks, RAG architectures, orchestration patterns, and AI application development.
- Experience building and deploying enterprise AI solutions that deliver measurable business outcomes.
- Strong facilitation and coaching abilities, with experience educating technical and non\-technical audiences.
- Comfortable working directly with business stakeholders to identify opportunities and redesign workflows.
- Proven ability to influence organizational change through hands\-on partnership and delivery.
- Experience mentoring and developing technical talent through real\-world project engagements.
- Strong understanding of responsible AI, governance, risk management, and production monitoring practices.
- Ability to translate complex technical concepts into practical business value and adoption strategies.
- Experience leading complex, cross\-functional initiatives that combine technology adoption, organizational change, and business transformation.
- Demonstrated ability to influence senior leaders and stakeholders across highly matrixed organizations.
- Experience developing scalable frameworks, playbooks, or best practices that enable broader organizational adoption.
- Strong executive communication skills with the ability to align technical solutions to business priorities and outcomes.
- Experience driving adoption of new technologies through hands\-on engagement, education, and change leadership.
- Experience with Copilot Studio, Azure AI, GitHub Copilot, Claude Code, or equivalent platforms preferred.
- Financial services, payments, or enterprise transformation experience preferred.
- Passion for developing others and creating sustainable capability within organizations.
Success in This Role
Success is not measured by the number of agents you build. Success is measured by the number of teams that can build without you.
You leave behind:
- New organizational capability.
- Repeatable AI patterns.
- Trained champions and practitioners.
- Demonstrated business value.
- Sustainable adoption of AI\-first ways of working.
- Scalable transformation practices that can be replicated across business units.
- Stronger organizational readiness and confidence in applying AI to business challenges.
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
Purchase, New York: $195,000 \- $323,000 USD
Arlington, Virginia: $196,000 \- $323,000 USD
Atlanta, Georgia: $170,000 \- $281,000 USD
Salary Context
This $170K-$323K 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($246K) sits 13% above the category median. Disclosed range: $170K to $323K.
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
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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