Senior AI Platform Engineer – LLMs & Foundation Models

Capital One National Association; McLean (VA); On-site; USD 162,000 - 185,000; Full time

Benefits offered by this job

Health benefits

Performance bonuses

Paid time off

401(k) matching

Job summary

Capital One is seeking an AI Engineer 3 to help build responsible and scalable AI systems for real-time customer experiences. You will work with engineers, researchers, PMs, and data teams to deploy foundation models, LLMs, and agent workflows across environments, while ensuring governance and safety practices.

The role focuses on design, development, and deployment of AI software, leveraging PyTorch, vector databases, and HuggingFace, with emphasis on performance, latency, and cost optimization.

Qualifications

• Bachelor’s or Master’s in a related field with 1–3+ years of AI/ML experience.
• 3+ years in Python/Go/Scala/CUDA/Java development for AI/ML systems.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components.
• Leverage Open Source and SaaS AI technologies; optimize model performance.
• Invent model optimization techniques for scalability and cost efficiency.
• Contribute to the technical vision and long-term AI system roadmap.
• Mentor junior AI engineers and promote engineering excellence.

Skills

Python

Go

Scala

CUDA

Java

Education

Bachelor's degree in CS/AI/EE/CE or related field

Master's degree in CS/AI/EE/CE or related field

Tools

AWS Ultraclusters

Huggingface

VectorDBs

PyTorch

Job description

Capital One is seeking an AI Engineer 3 to help build responsible and scalable AI systems for real-time customer experiences. You will work with engineers, researchers, PMs, and data teams to deploy foundation models, LLMs, and agent workflows across environments, while ensuring governance and safety practices.

The role focuses on design, development, and deployment of AI software, leveraging PyTorch, vector databases, and HuggingFace, with emphasis on performance, latency, and cost optimization.