Lead AI Platform Engineer

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.

Lead AI Platform Engineer

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  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.

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.

• 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…