AI Engineer

Samsara Samsara · Enterprise · San Francisco, CA · Business Systems

AI Engineer IV role focused on building, shipping, and scaling end-to-end AI-powered applications and GenAI capabilities within an enterprise setting. The role involves owning the full lifecycle from ideation to production deployment and iteration, integrating AI solutions with internal systems, and developing AI-powered solutions for various business functions. Requires strong Python and Generative AI experience, including LLMs, vector databases, prompt engineering, RAG, and agent development frameworks.

What you'd actually do

  1. Build, ship, and own end-to-end AI-powered applications, from ideation and prototyping through production deployment, monitoring, and ongoing iteration.
  2. Architect and scale company-wide GenAI capabilities and domain-specific business applications that are embedded into real operational workflows.
  3. Design and integrate AI solutions with internal systems and third-party platforms, including SaaS tools, APIs, data platforms, and enterprise systems.
  4. Develop and launch AI-powered solutions to improve efficiency and personalization across business functions such as Customer Support, People/HR, Legal, and Finance.
  5. Apply and scale software engineering excellence, including reliability, observability, and maintainability of production AI systems.

Skills

Required

  • Python
  • AI/ML fundamentals
  • Generative AI solutions
  • LLMs
  • vector databases
  • prompt engineering
  • embeddings
  • RAG systems
  • AI agent development frameworks
  • LangChain
  • LangGraph
  • CrewAI
  • LlamaIndex
  • OpenAI SDK
  • MCP
  • A2A
  • web applications and backend APIs development
  • deployment
  • monitoring
  • support of web applications and backend APIs

Nice to have

  • 0→1 MVP development
  • iterative prototyping
  • evolving prototypes into production-grade systems
  • staying up to date with industry news, AI development trends, tools, and best practices
  • SDLC best practices
  • modern AI development tools and patterns
  • cross-functional settings
  • translate business needs into technical solutions

What the JD emphasized

  • production-ready Generative AI solutions, with responsibility for reliability and performance
  • building and deploying production-ready Generative AI solutions
  • production deployment, monitoring, and iteration

Other signals

  • build, ship, and scale AI applications
  • own the full lifecycle of AI-powered systems
  • production deployment, monitoring, and iteration
  • company-wide GenAI capabilities
  • embedded into real operational workflows