Senior AI Engineer: Foundation Models & Agentic AI

Capital One; San Jose (CA); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One is seeking an AI Engineer 5 in San Jose, CA to help design and scale responsible AI systems powering real-time banking experiences. You will work with cross-functional teams to deploy AI components, including foundation models, agents, and vector search, across production pipelines.

The role emphasizes performance optimization, observability, governance, and cost efficiency, leveraging a broad stack such as AWS, Huggingface, PyTorch, and VectorDBs.

Qualifications

• Bachelor's or Master’s degree in a relevant field with several years of AI/ML experience.
• Strong programming skills in Python and at least one of Java/Go/Scala/CUDA.
• Experience with cloud platforms and AI system deployment.
• Knowledge of LLMs, inference, vector databases, and model governance.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products for customers and associates.
• Design, develop, test, deploy, and support AI software components (LLMs, agents, inference).
• Leverage Open Source and SaaS AI tools (AWS, Huggingface, PyTorch, VectorDBs).
• Optimize foundation models for performance, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap for AI systems at Capital One.
• Lead multi-model orchestration pipelines and governance reviews.

Skills

Python

Go

Scala

CUDA

Java

Education

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields

Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields

Tools

AWS

Google Cloud

Azure

Huggingface

PyTorch

VectorDBs

Job description

Capital One is seeking an AI Engineer 5 in San Jose, CA to help design and scale responsible AI systems powering real-time banking experiences. You will work with cross-functional teams to deploy AI components, including foundation models, agents, and vector search, across production pipelines.

The role emphasizes performance optimization, observability, governance, and cost efficiency, leveraging a broad stack such as AWS, Huggingface, PyTorch, and VectorDBs.