Lead AI Engineer

Capital One Capital One · Banking · New York, NY +2

Lead AI Engineer role focused on designing, developing, and deploying AI-powered products and foundational AI systems. The role involves working with LLM inference, similarity search, guardrails, model evaluation, and optimization techniques to improve scalability, cost, and latency of production AI systems. It requires strong engineering and AI expertise, with a focus on building and scaling AI solutions within an enterprise context.

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

  • deliver AI-powered products
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • state-of-the-art LLM optimization techniques
  • scalability, cost, latency, throughput
  • foundational AI systems

Other signals

  • Deploying AI solutions
  • Building AI infrastructure
  • Optimizing LLM performance