Lead Ai/ml Engineer (platform, Kubeflow)

Capital One Capital One · Banking · San Jose, CA +3

Lead AI/ML Engineer focused on building and optimizing AI platforms and infrastructure, including foundation model training, LLM inference, similarity search, guardrails, and evaluation. The role involves designing, developing, and deploying AI software components, leveraging various AI technologies, and improving the performance of large-scale 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
  • developing AI and ML algorithms or technologies
  • deploying scalable and responsible AI solutions on cloud platforms

Nice to have

  • Kubeflow
  • AWS Ultraclusters
  • Huggingface
  • VectorDBs
  • Nemo Guardrails
  • PyTorch
  • LLM Inference
  • Similarity Search
  • Guardrails
  • Memory
  • C++
  • C#
  • Golang
  • optimizing training and inference software
  • hardware utilization
  • latency
  • throughput
  • cost

What the JD emphasized

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

Other signals

  • design, develop, test, deploy, and support AI software components
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability, cost, latency, throughput