below marketmost similar roles pay hereabove market
This role pays more than
94%
of similar roles. Most pay
$162,000–$245,625
— the blue band above.
At the midpoint, this role pays about
$330k
versus about
$204k
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
155 open roles
on FindRole.
Listed pay typically runs
$122,000–$207,000
across 136 roles with salary data.
The Senior Principal AI Engineer- AI Center of Excellence joins the AI Center of Excellence as a technical expert and thought leader for enterprise AI infrastructure. This senior individual-contributor role involves shaping the architecture and engineering of secure, scalable, production-grade platforms. The engineer will design infrastructure for LLM training and inferencing, machine learning models, agentic AI, and emerging AI capabilities. Key responsibilities include leading the evolution of HPC, GPU and accelerated compute, rack-scale systems, private and hybrid cloud, Kubernetes, high-performance networking, and storage. The role requires deep expertise in distributed systems, SRE, observability, and security. The engineer must solve complex performance and scalability challenges while embedding Responsible AI, governance, and compliance into infrastructure by design within a highly regulated environment.
What does a AI Engineer earn in California?
Median $246150 from 84 postings across 12 companies.
Define the technical vision, architecture, and engineering standards for enterprise AI infrastructure and platforms.
Lead the architecture of HPC, GPU, rack-scale systems, and hybrid cloud environments.
Design infrastructure to support LLM training, inferencing, and agentic AI orchestration.
Solve complex performance and scalability challenges through hands-on prototyping and troubleshooting.
Establish engineering patterns for reliability, observability, automation, and capacity management.
Embed security, privacy, and Responsible AI governance into AI infrastructure by design.
Evaluate emerging AI and hardware technologies to influence enterprise platform roadmaps.
Mentor engineers through architecture reviews, reference designs, and knowledge sharing.
What we're looking for
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline.
Advanced degree preferred.
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.
MLOps
Generative AI
Agentic AI
Private Cloud
GPU
CPU
High Performance Storage
High Speed Networking
Predictive ML
Model Lifecycle Management
Data Center Design
Liquid Cooling
Observability
RFI
RFP
Generative AI
LLMs
Agentic AI
RAG
Python
Java
LangChain
Google Vertex AI
AutoGen
Semantic Kernel
Docker
Kubernetes
OpenShift
CI/CD
Microservices
Deep Learning
Natural Language Processing
Computer Vision
APIs