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 Senior Principal AI Engineer- AI Center of Excellence

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. 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.

Overview The AI Center of Excellence is seeking a Senior Principal AI Engineer – AI Infrastructure to serve as a technical expert and thought leader for Mastercard’s enterprise AI infrastructure.

This senior individual-contributor role will shape the architecture and engineering of secure, scalable, production-grade platforms supporting LLM model training and inferencing, machine learning models, large language models (LLMs), agentic AI, and emerging AI capabilities in production.

The successful candidate will bring deep expertise in high-performance computing (HPC), GPU and accelerated computing, rack-scale systems architecture, private and hybrid cloud, Kubernetes, high-performance networking and storage, observability, security, and reliability. They will work closely with AI Engineers, Data Scientists, Platform Engineers, Security, Architecture, and Product partners to deliver resilient, cloud-native AI platforms in a highly regulated environment.

About The Role

• Define the technical vision, architecture, and engineering standards for enterprise AI infrastructure and platforms.
• Lead the architecture and evolution of HPC, GPU and accelerated compute, rack-scale systems, private and hybrid cloud, Kubernetes, high-performance networking, storage, and AI platform services.
• Design infrastructure supporting LLM training and inferencing, model serving, machine learning workloads, agentic AI orchestration, and emerging AI capabilities.
• Solve complex infrastructure, performance, and scalability challenges through hands-on architecture, prototyping, performance engineering, and troubleshooting.
• Establish engineering patterns for reliability, resiliency, observability, automation, capacity management, security, and operational readiness.
• Embed security, privacy, Responsible AI, governance, compliance, and auditability into AI infrastructure by design.
• Evaluate emerging AI, HPC, GPU, networking, storage, and infrastructure technologies and influence enterprise architecture and platform roadmaps.
• Lead complex cross-functional technical initiatives and mentor engineers through architecture reviews, engineering standards, reference designs, and knowledge sharing.

All About You

• Years of experience in AI engineering, high-performance computing, platform engineering, infrastructure engineering, distributed systems, cloud technology, or related disciplines.
• Proven experience architecting and operating secure, mission-critical platforms at enterprise scale.
• Deep expertise in HPC, GPU/accelerated computing, rack-scale systems architecture, Kubernetes, private and hybrid cloud, high-speed networking, storage, and infrastructure automation.
• Experience supporting LLM training and inferencing, machine learning models, large language models, agentic AI, and production AI platforms.
• Strong knowledge of distributed systems, SRE, observability, resiliency, security, governance, compliance, and production operations.
• Demonstrated ability to lead complex architecture and engineering initiatives through technical expertise, influence, and cross-functional collaboration.
• Strong communication and mentoring skills with the ability to engage senior stakeholders and elevate technical expertise across engineering teams.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline; advanced degree preferred.

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Mastercard is a merit-based, in…