Senior AI Engineer: Foundation Models & Scalable AI

Capital One National Association; Richmond (VA); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One is seeking an AI Engineer to design and scale AI-powered financial products. You will work with engineers, researchers, and product managers to build foundation models, multi-model orchestration, and governance for responsible AI systems.

The role requires deep technical expertise in AI, ML, and cloud platforms, with a focus on performance, cost, and reliability across production environments.

Qualifications

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies
• or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies

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 (e.g., AWS Ultraclusters, Huggingface, PyTorch)
• Invent and optimize foundation model techniques to improve performance and cost
• Contribute to the technical vision and long-term roadmap of AI systems
• Design and optimize multi-model orchestration pipelines
• Lead cost-performance governance reviews across AI systems
• Mentor Principal and Manager-level AI engineers

Skills

AI engineering

Python

Go

Scala

CUDA

Java

System design

Mentoring

Leadership

Cross-functional collaboration

Education

Bachelor's in CS/AI/EE/CE

Master's in CS/AI/EE/CE

Tools

AWS

Google Cloud

Azure

HuggingFace

VectorDBs

PyTorch

Guardrails

Memory

Job description

Capital One is seeking an AI Engineer to design and scale AI-powered financial products. You will work with engineers, researchers, and product managers to build foundation models, multi-model orchestration, and governance for responsible AI systems.

The role requires deep technical expertise in AI, ML, and cloud platforms, with a focus on performance, cost, and reliability across production environments.