Deployed Engineer (atlanta)

LangChain LangChain · Data AI · Atlanta, GA · Deployed Engineering

LangChain is seeking a Deployed Engineer to work directly with companies building and running AI agents in production. This role involves co-architecting and co-building production AI agents, owning the technical win in pre-sales, deploying and operating agent-based applications, and advising customers on architecture and best practices. The role focuses on systems that real teams depend on in production, not demos or research.

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
  • designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
  • working directly with customers during POCs, architecture reviews, and technical evaluations
  • explain technical tradeoffs clearly
  • build trust with developer audiences
  • Take responsibility for outcomes, not just recommendations
  • bias toward action
  • operating AI agents in production

Nice to have

  • deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks
  • LLM evaluation, observability, or guardrails
  • cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
  • shipped and operated production software and are comfortable owning systems under real-world constraints

What the JD emphasized

  • systems that real teams depend on in production
  • technical win
  • operating AI agents in production

Other signals

  • make intelligent agents ubiquitous
  • building and operating agents at scale
  • systems that real teams depend on in production
  • achieving the technical win
  • co-designing agent architectures
  • operating agents reliably at scale