Lead AI Engineer (ai Foundations, LLM Core and Agentic Ai)

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

Lead AI Engineer role focused on building and optimizing AI systems, including foundation models, LLM inference, agentic AI, and related infrastructure. The role involves designing, developing, testing, deploying, and supporting AI software components, with a strong emphasis on improving performance, scalability, cost, and latency of large-scale production AI systems. It requires leveraging various AI technologies and contributing to the technical vision and roadmap for foundational AI systems.

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. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  3. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
  4. Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
  5. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.

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
  • 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
  • state-of-the-art LLM optimization techniques
  • large scale production AI systems
  • foundational AI systems
  • deploying scalable and responsible AI solutions

Other signals

  • LLM optimization
  • agentic AI
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability