Lead AI Engineer (ai Foundations)

Capital One Capital One · Banking · New York, NY +4

Lead AI Engineer focused on building and optimizing foundational AI systems, including LLM inference, similarity search, guardrails, and model evaluation, to enhance customer and associate experiences within a large enterprise.

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
  • LLM Inference
  • Similarity Search
  • VectorDBs
  • Guardrails
  • Memory
  • C++
  • C#
  • Golang
  • training optimization
  • inference optimization
  • hardware utilization
  • latency
  • throughput
  • cost optimization
  • AI research
  • AI systems

What the JD emphasized

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

Other signals

  • building and deploying proprietary solutions
  • advance the state of the art in science and AI engineering
  • build and deploy proprietary solutions that are central to our business
  • empower teams across Capital One to enhance their products with the transformative power of AI
  • deliver AI-powered products that change how our associates work and how our customers interact with Capital One
  • foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability
  • Invent and introduce state-of-the-art LLM optimization techniques to improve the 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