Lead AI Engineer: LLM Foundations & Production

Capital One; San Francisco (CA); On-site; USD 215,000 - 246,000; Full time

Benefits offered by this job

Health benefits

Well-being benefits

Job summary

Capital One in San Francisco, CA is seeking a Lead AI Engineer focused on AI foundations, LLM customization, finetuning, and reinforcement learning to build scalable, responsible AI systems for banking.

You will partner with cross‑functional engineers, research scientists, and product managers to design, train, deploy, and monitor AI components using Open Source and SaaS tools such as HuggingFace, PyTorch, VectorDBs, and guardrails, while pursuing state‑of‑the‑art optimization.

Qualifications

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

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, VectorDBs, Nemo Guardrails, PyTorch)
• Invent and apply state-of-the-art LLM optimization techniques for production systems
• Contribute to the technical vision and long-term roadmap of AI systems

Skills

Python

Go

Scala

Java

Cloud platforms

LLM Inference

Similarity Search

VectorDBs

Guardrails

Memory

Education

Bachelor’s degree in Computer Science or related fields

Master’s degree in Computer Science or related fields

Tools

AWS

Huggingface

PyTorch

VectorDBs

Job description

Capital One in San Francisco, CA is seeking a Lead AI Engineer focused on AI foundations, LLM customization, finetuning, and reinforcement learning to build scalable, responsible AI systems for banking.

You will partner with cross‑functional engineers, research scientists, and product managers to design, train, deploy, and monitor AI components using Open Source and SaaS tools such as HuggingFace, PyTorch, VectorDBs, and guardrails, while pursuing state‑of‑the‑art optimization.