Senior Lead AI Engineer (llm Customization and Finetuning)

Capital One Capital One · Banking · Cambridge, MA +3

Senior Lead AI Engineer focused on 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 requires leveraging AI technologies like Huggingface, VectorDBs, and PyTorch, and optimizing LLM performance for scalability, cost, latency, and throughput in production AI systems. The candidate should have a strong engineering and mathematics foundation, experience with cloud platforms, and the ability to lead and mentor teams.

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

What the JD emphasized

  • responsible and reliable AI systems
  • responsible and scalable ways
  • responsible and scalable AI solutions

Other signals

  • LLM Customization and Finetuning
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • performance — scalability, cost, latency, throughput