Senior Software Engineer, AI

Okta Okta · Enterprise · San Francisco, CA · BT Engineering Services-779

Senior Software Engineer, Applied AI at Okta, focusing on building end-to-end GenAI-powered applications, including UIs, API services, and backend orchestration. The role involves integrating LLM-based experiences, RAG pipelines, and developing frontend interfaces, with an emphasis on security, observability, and user experience for internal customers.

What you'd actually do

  1. Design and build end-to-end GenAI-powered applications, including web-based UIs, API services, and backend orchestration.
  2. Implement and integrate LLM-based experiences using frameworks like LangChain, LlamaIndex, and tools like OpenAI, Claude, or Gemini.
  3. Define, implement, and champion operational excellence standards (SLOs, observability, incident response frameworks) for all services deployed.
  4. Develop responsive, accessible, and modern frontend interfaces using frameworks like React or Vue — with a focus on usability, performance, and trust in AI outputs.
  5. Build and maintain a library of reusable frontend components and hooks that allow other business delivery teams to easily "drop in" GenAI capabilities into their own applications.

Skills

Required

  • Python development
  • cloud-based services using AWS, Docker, and RESTful APIs
  • frontend technologies like React, TypeScript, or Vue
  • LLM integration
  • RAG pipelines
  • prompt engineering
  • orchestration frameworks like LangChain or LlamaIndex
  • distributed systems
  • APIs
  • microservices
  • container orchestration (ECS/EKS)
  • cloud platforms (AWS/GCP/Azure)
  • secure coding
  • authentication/authorization
  • internal data governance best practices
  • collaboration across engineering, design, and product teams
  • user empathy
  • technical ownership

Nice to have

  • design systems
  • AI evaluation tooling
  • real-time application performance monitoring

What the JD emphasized

  • building intuitive, performant UIs
  • building AI/ML-driven applications
  • frontend technologies
  • LLM integration
  • RAG pipelines

Other signals

  • building user-facing and backend systems that leverage GenAI
  • integrating GenAI and intelligent automation into workflows
  • virtual agents, AI copilots, internal RAG services, and AI-augmented self-service portals