Senior Lead AI Engineer, Gen AI Platform

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

This role focuses on engineering and optimizing large-scale production AI systems, specifically within the Generative AI Platform at Capital One. Responsibilities include designing, developing, testing, deploying, and supporting AI software components such as foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, and observability. The role also involves inventing and applying state-of-the-art LLM optimization techniques to improve performance (scalability, cost, latency, throughput) of these systems. The ideal candidate is deeply technical, experienced in AI/ML algorithms and technologies, and skilled in programming languages like Python, Go, Scala, or Java, with a strong foundation in engineering and mathematics.

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
  • AI research
  • AI systems
  • communication skills
  • presentation skills

What the JD emphasized

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

Other signals

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput
  • production AI systems