Lead AI Engineer

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 EngineerMastercard 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.

Job Description
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 serves, 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.


Key responsibilities:
· 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
Skills, experience and attributes that would make you a strong candidate:

· 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.

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.