Lead AI Engineer

Mastercard is hiring a Lead AI Engineer in New York, United States. Level rates it ; you can apply on Mastercard.

Level's read on this job

Level rates this AI engineer role Little AI ●○○○: aI is not part of the work.
Tools and skills the posting cites include Databricks, fine tuning and MLOps.
Level classifies it as a lead role.
Level lists 931 other open roles at Mastercard.
The role was posted in the last few days.

AI in this role

Databricks

fine-tuningml-opsai-evaluation

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

Lead AI Engineer

Overview:

Mastercard is seeking a Lead AI Engineer to join a small, focused AI technology team led by one of Mastercard's Distinguished Engineers. The team will initially focus on delivering agentic technologies such as the Model Context Protocol (MCP) at scale and the use of open weight AI models. The team works in partnership with key technology stakeholders across the organization.

This is a hands-on engineering role. You will design and build the platform capabilities that let Mastercard adopt MCP and open weight models safely and at scale, working alongside more senior engineers who set the broader technical direction. We are looking for someone with strong knowledge of modern AI technology, solid software engineering and MLOps skills, good cloud platform experience, familiarity with identity technologies such as OAuth and a practical grasp of model evaluation.

In this role, you will help build the platform patterns through which Mastercard teams expose tools and data to agents over MCP, and through which the enterprise services, evaluates, and governs open weight models. You will contribute to the key technical decisions — gateway and connectivity patterns, identity and access controls, model-serving infrastructure, and evaluation — and turn them into working, well-engineered, production-grade services. You will work across the MCP estate and the OW model working group, partnering closely with the team's senior engineers and with central technology, security, and governance stakeholders.

About The Role:

· Contribute to the design and build of the components of Mastercard's enterprise MCP deployment, such as the security gateway, the MCP registry that governs what exists and who owns it, and reference server implementations that teams build from. Take these from design through to running production services.

· Implement the identity, authentication, and authorization patterns for MCP across the estate, working with the team's senior engineers and central security and identity teams so that agents, tools, and data are accessed securely and are auditable end to end.

· Help set and apply the standard for internal and external MCP servers — how tools and data are exposed, discovered and versioned, and the inspection, data-protection, rate-limiting, and kill-switch controls the gateway must enforce.

· Build and operate open weight model serving — stand up and scale self-hosted inference, package models for deployment, and help enable switching workloads across models and providers for cost, flexibility, and resilience.

· Build the evaluation and testing tooling that benchmarks candidate open weight models for quality, cost, latency, and safety, and produces the reproducible evidence that governance and security reviews need.

· Apply the MLOps practices that keep the platform reliable — CI/CD, infrastructure-as-code, observability, and model and service monitoring — so that what the team builds is maintainable and operable in production.

· Partner with central security, privacy, legal and AI governance teams, and with the MCP SIG and OW working group, implementing their requirements correctly for the platform's architecture.

· Keep current with a rapidly evolving landscape, such as new MCP developments, frontier open weight releases, and related agentic protocols, and help the team decide what to adopt, prototype, or wait on.

All About You:

· Strong knowledge of modern AI technology: LLMs and foundation models, agents, and the tooling and protocols (such as MCP) that connect them to enterprise systems.

· Good software engineering skills: you write production-quality, well-tested code and build services others can depend on, not one-off scripts or prototypes.

· Solid MLOps experience: CI/CD, infrastructure-as-code, containers, observability, and monitoring for models and services in production.

· Good cloud platform knowledge (AWS preferred; Azure or GCP valuable). Familiarity with a modern data/AI platform such as Databricks a bonus.

· Practical experience with model evaluation, including building benchmarks and tests that measure quality, cost, latency and safety, and producing reproducible results others can rely on.

· Hands-on experience serving or operating open weight models, self-hosting, and scaling inference, and ideally some fine-tuning or adaptation to proprietary data and workflows.

· Working knowledge of API security, identity, and access control, enough to implement MCP authentication and authorization correctly and partner effectively with security teams.

· Awareness of the governance context that shapes enterprise AI, such as model provenance and licensing, data sovereignty, and financial-services regulation.

· A clear communicator and collaborator who works well with senior engineers, hands-on peers, and specialist governance and security partner teams.

· Intense curiosity about technology in general, and AI in particular, and who can demonstrate their learning agility outside the bounds of a specific function.

· Lastly, a self-starter: once you have a clear guidance and direction, you don’t need to be hand-held to get to a solution or deliverable.

#AI1

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_accommodation@mastercard.com 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. THIS POSTING RELECTS ONE OR MORE CURRENT VACANCIES ON OUR TEAM, AND MASTERCARD INTENDS TO FILL THIS POSITION BY 02/07/2027. If the fill date has passed, Mastercard intends to fill this position as soon as possible.

Pay Ranges

Purchase, New York: $161,000 - $266,000 USD

Arlington, Virginia: $161,000 - $266,000 USD

New York City, New York: $168,000 - $277,000 USD

O'Fallon, Missouri: $140,000 - $231,000 USD

How we rate this

Lead AI Engineer at Mastercard rates 16 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Lead AI Engineer is an open job at Mastercard.
Level rates this role AI Level 1 of 4: Little AI.
The role is based in Purchase, New York.
The job was posted on November 9, 2026.
Seniority: lead.
Applications are handled by Mastercard. Apply on Mastercard

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningML OpsAI EvaluationDatabricks

Also common in AI engineer jobs at this level

Python Generative AI Machine learning SQL Java RAG

Pay

Typical AI engineer job: $184k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
How do you decide that one model's output is better than another's for a given task?
What's a project where you used Databricks hands-on?

Adapt your resume

List these exact terms on your resume: Fine-tuning, ML Ops, AI Evaluation, and Databricks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.

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