Senior Lead AI Engineer (gen AI Platform Services)

Capital One Capital One · Banking · San Jose, CA +1

Senior Lead AI Engineer role focused on building and scaling Gen AI Platform Services, including foundation model training, LLM inference, similarity search, guardrails, and model evaluation. The role involves optimizing AI systems for performance, cost, and latency, and contributing to the technical vision for foundational AI systems at Capital One.

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

  • deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
  • designing, developing, integrating, delivering, and supporting complex AI systems
  • lead and mentor an engineering team
  • influence cross-functional stakeholders
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • C++
  • C#
  • Golang
  • optimizing training and inference software
  • hardware utilization
  • latency
  • throughput
  • cost
  • staying abreast of the latest AI research
  • AI systems
  • apply novel techniques in production
  • communication and presentation skills
  • articulate complex AI concepts

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 foundational model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability