Senior Manager, AI Engineering (people Leader) (gen AI Platform Services)

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

Senior Manager, AI Engineering (People Leader) for Gen AI Platform Services at Capital One. This role involves overseeing the design, development, testing, deployment, and support of AI software components, including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, and governance. The candidate will make build-vs-buy decisions, invent optimization techniques for production AI systems, contribute to the technical vision and roadmap, and attract/retain AI talent.

What you'd actually do

  1. Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
  2. Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
  3. Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
  4. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  5. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Skills

Required

  • People leadership experience
  • Experience developing AI and ML algorithms or technologies
  • Experience deploying scalable and responsible AI solutions on cloud platforms

Nice to have

  • Experience managing and leading an engineering team
  • Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang
  • Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
  • Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers

What the JD emphasized

  • responsible and reliable AI systems
  • responsible and scalable ways
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • build-vs-buy decisions
  • Open Source and SaaS AI technologies
  • LLM optimization techniques
  • large scale production AI systems
  • foundational AI systems
  • deploying scalable and responsible AI solutions

Other signals

  • AI infrastructure
  • foundation models
  • LLM optimization
  • people leadership