Senior Lead AI Engineer

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

Senior Lead AI Engineer role focused on designing, developing, and deploying AI-powered products and foundational AI systems. Responsibilities include foundation model training, LLM inference, similarity search, guardrails, model evaluation, and optimization for scalability, cost, and latency. The role leverages various AI technologies and contributes to the technical vision and roadmap of AI systems at Capital One.

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. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
  3. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  4. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

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#
  • LLM Inference
  • Similarity Search
  • Guardrails
  • Memory
  • optimizing training and inference software
  • hardware utilization
  • latency
  • throughput
  • cost
  • AI research
  • AI systems

What the JD emphasized

  • responsible and reliable AI systems
  • responsible and scalable ways
  • responsible AI solutions

Other signals

  • design, develop, test, deploy, and support AI software components
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability, cost, latency, throughput
  • foundational AI systems