Lead AI Engineer, AI Foundations & LLM Core

Capital One; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One seeks a Lead AI Engineer to advance foundational AI systems and deliver AI-powered products used by millions of customers. You will partner with engineers, researchers, PMs, and product managers to design, build, and scale AI components across the enterprise.

You will work with a broad stack including PyTorch, vector databases, guardrails, and cloud platforms, focusing on performance, safety, and production readiness in a banking context.

Qualifications

• Bachelor's degree with 4+ years AI/ML experience or Master's with 2+ years experience.
• 4+ years programming in Python, Go, Scala, or Java.
• 6 years deploying AI solutions on cloud platforms (AWS/Google Cloud/Azure).
• Experience designing and delivering AI services and AI/ML algorithms.
• Experience with LLM inference, similarity search, vector databases, and guardrails.
• Ability to apply state-of-the-art techniques to production 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 (AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch).
• Invent and implement LLM optimization techniques for production scale.
• Contribute to the technical vision and long-term roadmap of foundational AI systems.

Skills

Python

Go

Java

Scala

C++

C#

Golang

AWS

Google Cloud

Azure

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

Huggingface

VectorDBs

PyTorch

Nemo Guardrails

AWS Ultraclusters

Job description

Capital One seeks a Lead AI Engineer to advance foundational AI systems and deliver AI-powered products used by millions of customers. You will partner with engineers, researchers, PMs, and product managers to design, build, and scale AI components across the enterprise.

You will work with a broad stack including PyTorch, vector databases, guardrails, and cloud platforms, focusing on performance, safety, and production readiness in a banking context.