Senior Lead AI Engineer

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

Senior Lead AI Engineer responsible for designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves optimizing LLM performance for scalability, cost, latency, and throughput, and contributing to the technical vision and roadmap of foundational AI systems. The role leverages various AI technologies and requires strong engineering and mathematical foundations.

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 software optimization
  • hardware utilization
  • latency
  • throughput
  • cost optimization
  • AI research
  • AI systems
  • communication
  • presentation

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
  • AI models and platforms empower teams
  • deliver AI-powered products
  • 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