Lead AI Engineer (ai Foundations, LLM Core and Agentic Ai)

Capital One Capital One · Banking · San Jose, CA +3

Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. Key responsibilities include optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and techniques.

What you'd actually do

  1. 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, etc.
  2. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  3. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
  4. Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
  5. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.

Skills

Required

  • Python
  • Go
  • Scala
  • Java
  • Computer Science
  • AI
  • Electrical Engineering
  • Computer Engineering

Nice to have

  • AWS
  • Google Cloud
  • Azure
  • Huggingface
  • VectorDBs
  • Nemo Guardrails
  • PyTorch
  • C++
  • C#
  • Golang

What the JD emphasized

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput

Other signals

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput