Distinguished AI Engineer (agentic AI Platform)

Capital One Capital One · Banking · San Francisco, CA +4

The role is for a Distinguished AI Engineer on the Agentic AI Platform team at Capital One. The primary focus is on building an enterprise Generative AI Platform that enables product teams to compose AI capabilities. This involves designing and implementing an agentic workflow framework, shared services (memory, guardrails, vector search, SDKs), and blueprints. The role emphasizes standardizing agentic workflows, improving developer experience through an end-to-end GenAI SDK/CLI, and ensuring trust and safety with central guardrail services. It also involves optimizing orchestration for performance and cost, and coaching/evangelizing the platform vision.

What you'd actually do

  1. You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement.
  2. A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them.
  3. Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%.
  4. Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents.
  5. You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend.

Skills

Required

  • Python
  • Go
  • Scala
  • Java
  • developing AI and ML algorithms or technologies

Nice to have

  • deploying scalable and responsible AI solutions on cloud platforms
  • Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen)
  • LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning)
  • designing mission-critical machine learning platforms
  • architecting, designing, developing, integrating, delivering, and supporting complex AI systems
  • lead and mentor multiple engineering teams and influence cross-functional stakeholders
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • C++
  • C#
  • Golang
  • optimizing training and inference software
  • GenAI or LLM-Powered application architectures in production
  • Responsible AI
  • data privacy
  • multi-tenant security patterns

What the JD emphasized

  • enterprise SLAs
  • zero Sev4 incidents

Other signals

  • enterprise Generative AI Platform
  • agentic workflow framework
  • shared services such as memory, guardrails, vector search, SDKs and blueprints
  • standardizing and automating agentic workflows
  • Developer experience is another cornerstone
  • central guardrail services
  • optimizing orchestration
  • auto scale agents to hit tenant SLAs
  • coach and evangelize