Lead AI Engineer (ai Foundations, LLM Customization and Finetuning)

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

Lead AI Engineer focused on AI Foundations, LLM Customization and Finetuning within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also emphasizes optimizing LLM performance for scalability, cost, and latency in production 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. 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
  • C++
  • C#
  • Golang
  • Huggingface
  • VectorDBs
  • Nemo Guardrails
  • PyTorch

What the JD emphasized

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability
  • LLM optimization techniques
  • scalable and responsible AI solutions
  • AI services
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • optimizing training and inference software

Other signals

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