Remote Principal AI Infrastructure Engineer

Mastercard

Arlington (VA)

On-site

USD 195,000 - 323,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

Mastercard is seeking a Principal AI Infrastructure Engineer to build and scale next-generation AI infrastructure supporting enterprise AI workloads. The role focuses on high-performance, scalable platforms across compute, storage, networking, and accelerators.

The ideal candidate is technically deep, innovative, and ready to drive enterprise AI capabilities at scale. You will lead the AI infrastructure roadmap, act as a product owner, and collaborate with data science, MLOps, and platform teams

Qualifications

Bachelor's degree in Computer Science, Electronics, or a related engineering field.
Significant experience in large-scale infrastructure, platform engineering, or systems architecture within a complex enterprise.
Proven experience owning infrastructure roadmaps and delivering in on-premise or private cloud environments at scale.
AI & Data Foundation: knowledge of AI/ML concepts, data science workflows, MLOps practices, and model lifecycle management.
Familiarity with Generative AI and Agentic AI architectures and their infrastructure needs.
Infrastructure Expertise: deep understanding of compute (CPU/GPU), high-performance storage, and networking.
Product & Vendor Mindset: ability to balance user needs, feasibility, cost, and evolution.
Enterprise Leadership: ability to influence in a large, matrixed organization and communicate with executives

Responsibilities

Lead AI infrastructure roadmap for Mastercard's on-premise private cloud and lifecycle management.
Act as product owner for AI infrastructure and translate data science needs into scalable platform capabilities.
Lead RFIs/RFPs for AI infrastructure components and drive vendor evaluation decisions.
Architect and scale infrastructure to train large models and support low-latency inference across global workloads.
Define and champion modern AI infrastructure standards including storage, networking, cooling, and power density.
Partner with data science, MLOps, and platform teams to align infrastructure with model development and deployment.
Collaborate with security, governance, compliance, and legal to meet regulatory and ethical AI standards.
Present trade-offs and ROI clearly to senior leaders and executives.