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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 Platform Engineer

Who is Mastercard?

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we are helping build a sustainable economy where everyone can prosper. We support digital payment choices that make transactions secure, simple, smart, and accessible. Our technology, innovation, partnerships, and networks deliver products and services that help people, businesses, and governments realize their greatest potential.

Overview

The AI Platform Engineering team builds, operates, and evolves Mastercard's enterprise AI platforms. Our mission is to provide scalable, secure, and reliable AI infrastructure that enables teams across Mastercard to accelerate the development and deployment of AI-powered solutions.

As a Lead AI Engineer, you will provide technical leadership in designing, implementing, and operating the foundational platforms supporting AI and machine learning workloads across the enterprise. Working at the intersection of platform engineering, cloud infrastructure, MLOps, and AI operations, you will deliver capabilities that enable teams to safely develop, deploy, observe, and scale AI solutions across public and private cloud environments.

You will help shape the platforms, tooling, operational practices, and infrastructure supporting the next generation of AI capabilities at Mastercard. You will address complex challenges involving scalability, reliability, observability, governance, automation, and customer enablement while influencing stakeholders and contributing to Mastercard's long-term AI platform direction.

About the Role

As a Lead AI Engineer, AI Platform Engineering, you will serve as a developing subject matter expert responsible for advancing the architecture, engineering, automation, and operational excellence of Mastercard's AI platforms. You will lead the technical design and delivery of complex capabilities, improve existing products and processes, and influence decisions across engineering, infrastructure, security, architecture, and governance teams.

The ideal candidate combines advanced software engineering and cloud platform expertise with experience supporting production AI and machine learning systems. You are passionate about technical leadership, automation, reliability, and building platforms that empower others to innovate. You are comfortable operating through influence, reviewing technical work, mentoring engineers, and translating complex requirements into scalable solutions.

Responsibilities

- Lead the design, development, and operation of enterprise platforms supporting machine learning, generative AI, agentic AI, and advanced analytics workloads.

- Translate business, engineering, and operational requirements into scalable platform solutions and implementation approaches.

- Design and maintain advanced AI and machine learning systems that meet enterprise requirements for security, reliability, availability, scalability, and performance.

- Own technical delivery across public and private cloud environments, identifying dependencies, resolving complex challenges, and influencing cross-functional decisions.

- Build and optimize services and workflows that simplify onboarding, development, deployment, operations, and lifecycle management for AI solutions.

- Develop infrastructure, tooling, and services supporting model training, evaluation, fine-tuning, deployment, inference, monitoring, and governance.

- Lead the implementation of repeatable, scalable, and secure MLOps capabilities and AI development workflows.

- Build and optimize data ingestion, preprocessing, and feature engineering workflows supporting model training and inference.

- Design observability capabilities including telemetry, performance monitoring, logging, alerting, operational analytics, model monitoring, and drift detection.

- Ensure the operational stability and scalability of AI platforms, driving improvements to reliability, resilience, efficiency, security, and customer experience.

- Lead troubleshooting, root cause analysis, and incident response for complex platform issues and translate lessons learned into sustainable improvements.

- Influence platform architecture through design discussions, reviews, prototypes, tradeoff analysis, and technical recommendations.

- Partner with engineering and product teams to understand requirements, guide solution design, enable adoption, and accelerate delivery of AI-powered products.

- Collaborate with infrastructure, security, architecture, risk, and governance teams to align capabilities with enterprise standards, controls, ethical guidelines, and regulatory requirements.

- Evaluate emerging AI technologies and industry trends and recommend improvements to Mastercard's AI ecosystem and roadmap.

- Drive engineering best practices across Infrastructure as Code, CI/CD, automated testing, secure development, documentation, and operational excellence.

- Guide and mentor engineers through design guidance, technical reviews, problem-solving support, and knowledge sharing.

All About You

Required Qualifications

- Experience designing, building, and operating cloud-native platforms or large-scale distributed systems in enterprise environments.

- Strong experience delivering solutions across public and private cloud environments.

- Experience deploying and managing containerized workloads using Kubernetes or OpenShift.

- Advanced software engineering and automation experience using Python.

- Experience leading the technical design and delivery of complex initiatives involving multiple teams or stakeholders.

- Experience implementing CI/CD pipelines, Infrastructure as Code, automated testing, and modern DevOps practices.

- Experience designing or supporting production AI, machine learning, data platforms, or distributed systems.

- Strong understanding of MLOps, model lifecycle management, production inference, and AI system operations.

- Experience implementing telemetry, monitoring, logging, alerting, and operational analytics.

- Ability to diagnose complex issues, lead root cause analysis, and implement sustainable corrective actions.

- Ability to assess architectural options, explain technical tradeoffs, and influence cross-functional decisions.

- Ability to communicate effectively with technical and non-technical stakeholders.

- Experience guiding, mentoring, or reviewing the work of other engineers.

Preferred Qualifications

- Experience with enterprise generative AI platforms and large language model workloads.

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