Mastercard
Financial Services
LeadAIengineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead AI engineer at Mastercard. Skills: AI Engineering, Machine Learning Engineering, Applied Data Science. Lead development of AI systems. Lead development of agentic systems”
Industry & Context.
Problem-solving skills; Ambiguous requirements handling
Abide by security policies, Ensure confidentiality, Ensure integrity, Report security violations, Complete security trainings
What They're Looking For.
Must Have
5+ years AI engineer experience, 5+ years ML engineer experience, 5+ years senior software engineer experience, Production AI systems experience, Solid software engineering foundations, Solid system design foundations, Solid distributed systems foundations, Production ML models experience, Operate ML models at scale experience, Data engineering experience, ML engineering experience, Applied data science tasks experience, Large-scale data platforms experience, Modern ML/AI tooling experience, Problem-solving skills, Ability to influence technical direction, Clear communication skills, Comfort collaborating across functions
Nice to Have
Built AI applications in production, Built agentic applications in production, Operated AI applications in production, Operated agentic applications in production, Agent-based systems implementation experience, LLM-powered systems implementation experience, Intuition for reliability in AI systems, Intuition for observability in AI systems, Intuition for failure handling in AI systems, Move fluidly between engineering execution and modeling, Raised technical bar for teams
What You'll Do.
Lead development of AI systems
Lead development of agentic systems
Build ML/AI pipelines
Operate ML/AI services
Operate ML/AI pipelines
Design ML engineering capabilities
Implement ML engineering capabilities
Productionise experiments
Contribute to data preparation
Contribute to feature engineering
Contribute to experimentation
Contribute to modelling
Drive technical design reviews
Provide mentorship to engineers
Provide mentorship to data scientists
Ensure AI solutions meet standards
Collaborate with platform teams
Collaborate with security teams
Collaborate with infrastructure teams
How You'll Work.
Team & Collaboration
Applied AI teams; Data Science teams; Product teams; Platform teams; Security teams; Infrastructure teams
Communication Scope
Clear communication
Full Job Description
**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 producti
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