Lead AI Engineer, AI Foundations

Capital One Capital One · Banking · New York, NY +3

Lead AI Engineer focused on building and optimizing AI Foundations, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. The role involves designing, developing, testing, deploying, and supporting AI software components, with a strong emphasis on improving performance (scalability, cost, latency, throughput) of large-scale production AI systems using state-of-the-art LLM optimization techniques. The role also touches on agentic systems through guardrails and similarity search, and model evaluation.

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
  • private cloud
  • 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

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