Sr Software Engineer, Cribl AI

Cribl Cribl · Enterprise · CA · Engineering

Senior Software Engineer to productionize, launch, and operate AI-based technology integrations into Cribl's core products, focusing on AI agent-driven product experiences, RAG, prompt engineering, and integrating Generative AI with observability data.

What you'd actually do

  1. Productionize, launch, and operate AI-based technology integrations into Cribl’s core products with the goal of solving real customer problems
  2. Partner with product & design leaders to prototype and experiment with new AI features
  3. Stay up-to-date with the latest AI technologies and trends
  4. This position will require stand-by, on-call, or off-hours duties

Skills

Required

  • 5+ years of professional software engineering experience building AI features end to end in production across product, backend, and model integration layers
  • Proven fullstack experience, including designing and scaling React-based UIs and backend services or APIs, with strength in backend and systems thinking
  • Deep expertise in TypeScript or JavaScript plus experience with at least one backend language or runtime such as Node.js, Go, Java, or similar
  • Professional experience building AI agent-driven product experiences, including conversational interfaces, tool-calling patterns, RAG, prompt engineering, evaluation or guardrail techniques, and agent orchestration connected to reliable backend and data systems
  • Professional experience with Model Context Protocols (MCPs), agent frameworks, orchestration layers, and integrating external tools and data sources into LLM-based systems
  • Machine learning experience, ideally in applied settings where models or AI systems were shipped, integrated, evaluated, or operated in production
  • Familiarity with data and infrastructure patterns for AI systems, including databases, APIs, observability, and integrating external tools, services, and data sources in production environments
  • Ability to problem-solve from first principles, make sound engineering trade-offs aligned with product and business goals, and drive work independently through ambiguity
  • Excellent communication skills, both verbal and written, with the ability to explain complex technical topics to cross-functional stakeholders in a remote or distributed environment

What the JD emphasized

  • building AI features end to end in production
  • Professional experience building AI agent-driven product experiences
  • Professional experience with Model Context Protocols (MCPs), agent frameworks, orchestration layers, and integrating external tools and data sources into LLM-based systems
  • Machine learning experience, ideally in applied settings where models or AI systems were shipped, integrated, evaluated, or operated in production

Other signals

  • Productionize AI integrations
  • Build AI agent-driven product experiences
  • Integrate Generative AI into product suite