Description
Mastercard's AI Center of Excellence is seeking a Senior Principal AI Engineer – AI Infrastructure to serve as a technical expert and thought leader for enterprise AI infrastructure.
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.
Key responsibilities:
- 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.
Requirements:
- 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.
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
- 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