Senior Software Engineer, AI

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

Senior Software Engineer, Applied AI at Okta. This role focuses on building end-to-end GenAI-powered applications, including UIs, API services, and backend orchestration. It involves integrating LLM-based experiences, developing frontend interfaces, building RAG pipelines, and ensuring operational excellence for AI services within an enterprise context.

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

  • full-stack development
  • AI/ML-driven applications
  • frontend technologies like React, TypeScript, or Vue
  • Python development
  • cloud-based services using AWS, Docker, and RESTful APIs
  • 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)

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
  • distributed systems, APIs, microservices, container orchestration (ECS/EKS), and cloud platforms (AWS/GCP/Azure)

Other signals

  • integrating GenAI and intelligent automation into workflows
  • building user-facing and backend systems that leverage GenAI
  • LLM-based experiences
  • retrieval-augmented generation (RAG) pipelines