Senior AI Engineer: LLM Customization & Foundations

Capital One; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is hiring a Lead AI Engineer in New York, NY to build and optimize AI foundations and customization for large language models. You will work with cross-functional teams to deploy AI-powered features for customers and associates, focusing on scalable, responsible AI systems and observability.

The role emphasizes cutting-edge techniques in LLM optimization, vector databases, and guardrails, with strong engineering and mathematical foundations.

Qualifications

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4+ years of AI/ML experience or Master’s with 2+ years of AI/ML experience.
• 4+ years of programming experience with Python, Go, Scala, or Java.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products impacting associates and customers.
• Design, develop, test, deploy, and support AI software components (foundation model training, LLM inference, vector similarity).
• Leverage Open Source and SaaS AI tech stacks (AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch).
• Invent and apply LLM optimization techniques to improve performance, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap of foundational AI systems at Capital One.

Skills

Python

Java

Go/Scala

LLM Inference

Similarity Search

VectorDBs

Guardrails

Cloud Platforms

Education

Bachelor's degree in CS/AI/EE/Engineering

Master's degree in CS/AI/Engineering

Tools

AWS

Google Cloud

Azure

Huggingface

Job description

Capital One is hiring a Lead AI Engineer in New York, NY to build and optimize AI foundations and customization for large language models. You will work with cross-functional teams to deploy AI-powered features for customers and associates, focusing on scalable, responsible AI systems and observability.

The role emphasizes cutting-edge techniques in LLM optimization, vector databases, and guardrails, with strong engineering and mathematical foundations.