Lead AI Platform Engineer

US: Mastercard Technologies, LLC; New York (NY); On-site; USD 168,000 - 277,000; Full time

Benefits offered by this job

401k with match

Paid leaves

On-site fitness facilities

Tuition reimbursement

Job summary

Mastercard’s AI Platform Engineering team builds and operates enterprise AI platforms to enable scalable, secure AI workloads across cloud environments.

The Lead AI Engineer will guide architecture, automation, and governance for AI deployments, mentoring engineers and shaping platform direction for reliable, observable AI capabilities.

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.

Responsibilities

• Lead design, development, and operation of enterprise AI platforms and services.
• Translate requirements into scalable platform solutions and implementation approaches.
• Design and maintain AI/ML systems with enterprise-level security, reliability, and performance.
• Own technical delivery across cloud environments and influence cross-functional decisions.
• Build and optimize data ingestion, preprocessing, and feature engineering workflows for model training and inference.

Skills

Cloud-native platforms

Python

MLOps

CI/CD

Platform leadership

Observability

Security/compliance

Cross-functional collaboration

Tools

Kubernetes

OpenShift

Docker

Helm

GitOps

Terraform

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 Platform Engineer

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.
• Experience with model evaluation, fine-tuning, guardrails, responsible AI controls, and model governance.
• Experience with model observability, drift detection, telemetry, and operational analytics.
• Experience with AI serving infrastructure, inference platforms, or AI gateways.
• Experience supporting GPU-based workloads and accelerated computing environments.
• Experience with OpenShift, Kubernetes, Docker, Helm, GitOps, and Infrastructure as Code.
• Experience with enterprise platform engineering, developer enablement, and self-service capabilities.
• Familiarity with vector databases, retrieval-augmented generation, AI gateways, agentic systems, and model routing.
• Experience operating in regulated environments requiring strong security, governance, risk management, resiliency, and compliance controls.

Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact [e-mail] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

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.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience.

Benefits

• insurance (including medical, prescription drug, dental, vision, disability, life insurance)
• flexible spending account and health savings account
• paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave)
• 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire
• 10 annual paid U.S. observed holidays
• 401k with a best-in-class company match
• deferred compensation for eligible roles
• fitness reimbursement or on-site fitness facilities
• eligibility for tuition reimbursement
• and many more
• 56 hours of Paid Sick and Safe Time (interns)
• jury duty leave (interns)
• on-site fitness facilities in some locations (interns)

THIS POSTING RELECTS ONE OR MORE CURRENT VACANCIES ON OUR TEAM, AND MASTERCARD INTENDS TO FILL THIS POSITION BY 01/26/2027. If the fill date has passed, Mastercard intends to fill this position as soon as possible.

Pay Ranges

• New York City, New York: $168,000 - $277,000 USD
• Arlington, Virginia: $161,000 - $266,000 USD
• Atlanta, Georgia: $140,000 - $231,000 USD
• Austin, Texas: $140,000 - $231,000 USD
• Boston, Massachusetts: $161,000 - $266,000 USD
• O'Fallon, Missouri: $140,000 - $231,000 USD
• Purchase, New York: $161,000 - $266,000 USD

Everyone wants easier ways to pay; we invent them. Checkout lines are slow; we speed them along. Merchants want more sales; we give them data and insights. People need financial access; we connect them. Corporate purchasing is complicated; we make it simple. Commuters are busy; we speed them on their way. Governments need greater efficiencies; we help create them. Small businesses are virtual; we give them access to a world of buyers. Retailers want to fight fraud; we provide the tools.