Lead AI Engineer: Scalable LLMs & AI Systems

Capital One; Cambridge (MA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One is seeking a Lead AI Engineer to collaborate with engineers, researchers, and product managers to build AI-powered capabilities that transform how associates work and how customers interact with Capital One.

You will design, develop, deploy, and support AI software components, use a broad stack of Open Source and SaaS AI technologies, and apply state-of-the-art optimization to improve scale, latency, and cost while shaping the long‑term AI foundation roadmap.

Qualifications

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

Responsibilities

• Partner with cross-functional team to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Skills

Python

Go

Scala

Java

Education

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

Master's degree in CS/AI/EE/CE

Tools

AWS Ultraclusters

Huggingface

VectorDBs

Nemo Guardrails

PyTorch

Job description

Capital One is seeking a Lead AI Engineer to collaborate with engineers, researchers, and product managers to build AI-powered capabilities that transform how associates work and how customers interact with Capital One.

You will design, develop, deploy, and support AI software components, use a broad stack of Open Source and SaaS AI technologies, and apply state-of-the-art optimization to improve scale, latency, and cost while shaping the long‑term AI foundation roadmap.