Lead AI Engineer

Mastercard is hiring a Lead AI Engineer in Dublin, Ireland. Level rates it ; you can apply on Mastercard.

Level's read on this job

Little AI ●○○○ is how Level rates this AI engineer role: aI is not part of the work.
The posting mentions Databricks, fine tuning and MLOps among its tools and skills.
Level classes it as a lead role.
Mastercard has 923 other open roles on Level.
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

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.

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.

How we rate this

Lead AI Engineer at Mastercard rates 10 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 Dublin, Ireland (One South County).
The job was posted on November 10, 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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