Senior AI Infrastructure Architect

Mastercard

Arlington (VA)

On-site

USD 195,000 - 323,000

Full time

14 days+

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Job summary

Mastercard is seeking a Principal AI Platform Engineer to lead the AI infrastructure roadmap for our on-prem private cloud and drive scalable platforms across compute, storage, and networking. You will partner with data science, MLOps, and security teams to ensure architecture supports both predictive ML and generative AI workloads at enterprise scale.

You will own RFIs/RFPs, define standards for high-performance storage and cooling, and articulate ROI and trade-offs to executives.

Qualifications

  • Bachelor’s degree in CS or related field.
  • Significant experience in large-scale infrastructure/platform engineering.
  • Experience owning infrastructure roadmaps in on-premise/private cloud.
  • Good working knowledge of AI/ML concepts, data science workflows, MLOps, and model lifecycle.

Responsibilities

  • Lead AI infrastructure roadmap for Mastercard’s on-prem private cloud.
  • Act as product owner for AI infrastructure translating needs into scalable platform capabilities.
  • Lead RFIs/RFPs for AI infrastructure components with defined criteria.
  • Architect and scale infrastructure for training large models with low-latency inference across global workloads.
  • Define modern AI infra standards including storage, networking, and data center design.
  • Collaborate with data science, MLOps, and security teams to align infrastructure with lifecycle needs.
  • Engage with security, governance, and legal teams to meet regulatory and ethical standards.
  • Communicate trade-offs and ROI to senior leadership with structured narratives.

Skills

AI infrastructure
MLOps
RFIs/RFPs
Executive presentations

Education

Bachelor’s degree in Computer Science or related engineering

Job description

Mastercard is seeking a Principal AI Platform Engineer to lead the AI infrastructure roadmap for our on-prem private cloud and drive scalable platforms across compute, storage, and networking. You will partner with data science, MLOps, and security teams to ensure architecture supports both predictive ML and generative AI workloads at enterprise scale.

You will own RFIs/RFPs, define standards for high-performance storage and cooling, and articulate ROI and trade-offs to executives.

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