Lead AI engineer

Reposted 8 Days Ago
Be an Early Applicant
Dublin, IRL
Hybrid
Senior level
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
We are a global technology company in the payments industry.
The Role
The role involves leading the development of AI systems, managing ML services, collaborating with various teams, and ensuring compliance with performance and security standards.
Summary Generated by Built In
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
Who is Mastercard?
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realise their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Overview
The CNPF Data & AI organisation is looking for a Lead AI Engineering Engineer to drive hands-on delivery of applied AI and agentic capabilities across our platforms. This role sits at the intersection of software engineering, machine learning engineering, and applied data science, with a strong emphasis on building production-grade AI systems.
This is a senior individual contributor and technical leadership role. You will lead by example through deep hands-on engineering, influence technical direction, and partner closely with Applied AI, Data Science, and Product teams to take AI solutions from experimentation to secure, scalable production.
Role
• Lead hands-on development of AI and agentic systems from design through production deployment• Build and operate ML/AI services, pipelines, and APIs using strong software engineering practices• Design and implement ML engineering capabilities such as model serving, monitoring, evaluation, and retraining• Partner with data scientists to productionise models and experiments efficiently• Contribute directly to data preparation, feature engineering, experimentation, and modelling when required• Drive technical design reviews and provide mentorship to engineers and data scientists• Ensure AI solutions meet Mastercard standards for performance, reliability, security, and governance• Collaborate closely with platform, security, and infrastructure teams to ship responsibly at scale
All about you
• Strong experience as a hands-on AI engineer, ML engineer, or senior software engineer working on production AI systems• Solid foundations in software engineering, system design, and distributed systems• Proven experience productionising machine learning models and operating them at scale• Comfortable working across data engineering, ML engineering, and applied data science tasks• Experience with large-scale data platforms and modern ML/AI tooling• Strong problem-solving skills and ability to work with ambiguous requirements• Ability to influence technical direction without formal people management responsibility• Clear communication skills and comfort collaborating across functions
What Makes You Stand Out• You have built and operated AI or agentic applications that run in real production environments• Hands-on experience implementing agent-based or LLM-powered systems beyond simple POCs• Strong intuition for reliability, observability, and failure handling in AI systems• Ability to move fluidly between engineering execution and applied modeling when needed• Track record of raising the technical bar for teams through code, design, and mentorship
Corporate Security Responsibility
Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organisation and therefore it is expected that the successful candidate 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• Complete all mandatory security trainings in accordance with Mastercard's guidelines
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.

Skills Required

  • Strong experience as a hands-on AI engineer, ML engineer, or senior software engineer
  • Solid foundations in software engineering, system design, and distributed systems
  • Proven experience productionising machine learning models and operating them at scale
  • Comfortable working across data engineering, ML engineering, and applied data science tasks
  • Experience with large-scale data platforms and modern ML/AI tooling
  • Strong problem-solving skills and ability to work with ambiguous requirements
  • Ability to influence technical direction without formal people management responsibility
  • Clear communication skills and comfort collaborating across functions

What the Team is Saying

Jenny
Mastercard

Mastercard Compensation & Benefits Highlights

  • Retirement Support Company information highlights a 10% retirement match on U.S. roles, positioned as best‑in‑class and well above typical large‑employer benchmarks. This level of employer contribution materially strengthens long‑term savings.
  • Leave & Time Off Breadth U.S. postings list 25 vacation days, 5 personal days, 10 company holidays, 80 hours of paid sick/safe time, and up to 20 days of bereavement. A minimum of 16 weeks paid new‑parent leave (including adoption and foster) further expands paid time away.
  • Parental & Family Support Benefits include a minimum of 16 weeks paid new‑parent leave and family‑building support such as fertility, adoption, and surrogacy where legally available. Dependent scholarships, counseling, and protection benefits contribute additional family support.

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The Company
HQ: Purchase, NY
38,800 Employees
Year Founded: 1966

What We Do

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient 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.

Why Work With Us

We live the Mastercard Way: creating value in the communities we touch, growing together through the opportunities we see, and moving fast to innovate and scale. Our collaborative culture and our passionate people are the key to what we do, driving meaningful change as one team and connecting everyone to priceless possibilities.

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About our Teams

Mastercard Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

In our ongoing workplace evolution, we’ve introduced hybrid work, Work-From-Elsewhere Weeks and Meeting-Free Days.

Typical time on-site: 3 days a week
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