Distinguished AI Engineer (agentic AI Platform)

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

The role is for a Distinguished AI Engineer focused on building an enterprise Generative AI Platform. The engineer will design the agentic workflow framework, shared services (memory, guardrails, vector search, SDKs), and blueprints to enable product teams to compose AI capabilities. Key responsibilities include evaluating agentic frameworks, developing an end-to-end GenAI SDK/CLI, implementing central guardrail services, optimizing orchestration for performance, and mentoring other engineers. The role emphasizes creating scalable, safe, and explainable AI solutions for millions of users.

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
  • designing mission-critical machine learning platforms
  • architecting, designing, developing, integrating, delivering, and supporting complex AI systems
  • leading and mentoring multiple engineering teams
  • influence cross-functional stakeholders up to the VP level
  • developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory)
  • optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
  • leading GenAI or LLM-Powered application architectures in production
  • Responsible AI
  • data privacy
  • multi-tenant security patterns

Nice to have

  • AWS
  • Google Cloud
  • Azure
  • LangChain
  • CrewAI
  • Semantic Kernel
  • AutoGen
  • Google Cloud Vertex AI
  • Amazon SageMaker
  • Azure Machine Learning
  • C++
  • C#
  • excellent communication and presentation skills

What the JD emphasized

  • enterprise SLAs
  • enterprise Generative AI Platform
  • agentic workflow framework
  • central guardrail services
  • zero Sev4 incidents

Other signals

  • enterprise Generative AI Platform
  • agentic workflow framework
  • shared services such as memory, guardrails, vector search, SDKs
  • translate foundation model power into production grade applications