This role pays more than
88%
of similar roles. Most pay
$169,550–$246,150
— the shaded band above.
At the midpoint, this role pays about
$259k
versus about
$208k
for comparable roles.
Based on 240 similar postings.
Employer
About Mastercard
Mastercard is a global technology company in the payments industry, processing transactions between financial institutions and merchants using its extensive network of credit, debit, and prepaid card products. Industry: Payments Technology & Financial Services
Mastercard currently has
116 open roles
on FindRole.
Listed pay typically runs
$122,000–$207,000
across 104 roles with salary data.
The Principal AI Platform Engineer joins the AI Center of Excellence to build and scale next-generation infrastructure supporting growing AI workloads. This role involves leading the infrastructure roadmap for an on-premise private cloud, managing the lifecycle of compute, storage, and networking components, and acting as a product owner to translate data science needs into scalable platform capabilities. The engineer will architect systems capable of training large-scale predictive and generative models on petabytes of data while supporting low-latency inference. Key technical requirements include expertise in CPU, GPU, high-performance storage, and advanced data center designs like liquid cooling. Candidates must possess a deep understanding of MLOps practices, Generative AI, and Agentic AI architectures. The role requires navigating complex enterprise environments to ensure infrastructure meets regulatory standards while managing RFI and RFP processes for critical hardware components.
Lead the AI infrastructure roadmap for on-premise private clouds including strategy, design, and lifecycle management.
Act as a product owner to translate data science needs into scalable platforms for predictive and generative AI workloads.
Manage RFI and RFP processes to evaluate and select hardware components like CPUs, GPUs, storage, and networking.
Architect infrastructure capable of training large-scale models on petabytes of data while supporting low-latency inference.
Define and champion technical standards for high-performance storage, high-speed networking, and advanced data center designs.
Align infrastructure choices with MLOps practices to ensure successful model development, deployment, and observability.
Ensure all AI solutions comply with corporate security, risk, governance, and ethical AI standards.
Present complex technical trade-offs and ROI analyses to senior leadership and executive stakeholders.
What we're looking for
Bachelor’s degree in Computer Science, Electronics, or a related engineering field.
Significant professional experience in large scale infrastructure, platform engineering, or systems architecture within a complex enterprise environment.
Proven experience owning infrastructure roadmaps and driving delivery in on-premise or private cloud environments at scale.
Working knowledge of AI/ML concepts, data science workflows, MLOps practices, and model lifecycle management.
Familiarity with Generative AI and Agentic AI architectures, including their specific infrastructure, networking, and latency requirements.
Deep understanding of compute (CPU/GPU), high performance storage, and networking technologies for modern AI platforms.
Experience with high density, high power AI infrastructure, including cooling, power, and data center design considerations.
Demonstrated experience running RFIs/RFPs and evaluating vendor proposals to make informed investment decisions.
Remote (Palo Alto, CA)+17 days ago
$242,634–$499,541
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