Deployed Engineer (seattle)

LangChain · Data AI · Seattle, WA · Deployed Engineering

This role focuses on co-architecting and co-building production AI agents with customer engineering teams, owning the technical win in pre-sales, and helping customers deploy and operate agent-based applications. It involves advising customers on architecture, best practices, and roadmap decisions, running technical demos and trainings, and surfacing field feedback. The role requires strong Python/JavaScript, systems fundamentals, and experience designing agent-based applications beyond simple API calls, including multi-step workflows and orchestration.

What you'd actually do

  1. Co-architect and co-build production AI agents with customer engineering teams
  2. Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
  3. Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
  4. Advise customers post-sale on architecture, best practices, and roadmap-level decisions
  5. Run technical demos, trainings, and workshops for developer audiences

Skills

Required

  • Python
  • JavaScript
  • systems fundamentals
  • designing agent-based or LLM-powered applications
  • multi-step workflows
  • orchestration
  • failure handling
  • working directly with customers
  • explaining technical tradeoffs clearly
  • building trust with developer audiences
  • take responsibility for outcomes

Nice to have

  • deployed AI agents in production
  • LangChain
  • LangGraph
  • LLM evaluation
  • observability
  • guardrails
  • cloud environments (AWS, GCP, Azure)
  • containers
  • basic Kubernetes concepts
  • shipped and operated production software

What the JD emphasized

  • systems that real teams depend on in production
  • agent-based or LLM-powered applications
  • multi-step workflows
  • orchestration
  • failure handling
  • operating AI agents in production

Other signals

  • building and running AI agents in production
  • co-designing agent architectures
  • operating agents reliably at scale
  • systems that real teams depend on in production
  • deploy and operate agent-based applications
  • multi-step workflows
  • orchestration
  • failure handling