Senior Lead AI Engineer, GenAI Platform & Agentic Systems

Capital One; San Francisco (CA); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One is seeking a Senior Lead AI Engineer to advance responsible and scalable AI systems within the GenAI Platform Services group in San Francisco. You will help shape the architecture, improve model training and inference, and drive platform-level optimization across banking products.

You will work with engineers, researchers, PMs, and product teams to deploy AI software components, apply guardrails, and optimize performance and cost.

Qualifications

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI/ML algorithms or technologies
• Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI/ML algorithms or technologies
• At least 6 years of experience programming with Python, Go, Scala, or Java

Responsibilities

• Partner with a cross-functional team of engineers, researchers, PMs to deliver AI-powered products
• Design, develop, test, deploy, and support AI software components
• Leverage a broad stack of Open Source and SaaS AI technologies
• Invent and introduce state-of-the-art LLM optimization techniques
• Contribute to the technical vision and long-term roadmap of foundational AI systems

Skills

Python

Go

Java

Scala

ML/AI

Leadership

Education

Bachelor's degree in Computer Science/AI/Engineering

Master's degree in Computer Science/AI/Engineering

Tools

AWS Ultraclusters

Huggingface

VectorDBs

Nemo Guardrails

PyTorch

Job description

Capital One is seeking a Senior Lead AI Engineer to advance responsible and scalable AI systems within the GenAI Platform Services group in San Francisco. You will help shape the architecture, improve model training and inference, and drive platform-level optimization across banking products.

You will work with engineers, researchers, PMs, and product teams to deploy AI software components, apply guardrails, and optimize performance and cost.