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LangChain

Deployed Engineer (Seattle)

Seattle, WAfulltimemidAdded 1 month ago

About this role

LangChain is seeking a Deployed Engineer to work hands-on with enterprise customers building and operating AI agents in production. You'll co-architect agent systems, lead technical evaluations, and bridge engineering with go-to-market by helping teams move from prototypes to reliable, scalable deployments using the LangChain platform.

What you'll do

  • Co-architect and build production AI agents alongside customer engineering teams
  • Own technical wins in pre-sales by designing POCs and guiding evaluations
  • Deploy and operate agent applications including conversational systems and multi-step workflows
  • Advise customers post-deployment on architecture, best practices, and strategic decisions
  • Run technical demos, trainings, and workshops for developer audiences
  • Surface field insights and contribute reusable patterns, cookbooks, and example code

What they're looking for

  • 5+ years software engineering, customer engineering, or solutions engineering experience
  • Python and JavaScript proficiency with strong systems fundamentals
  • LLM and agent-based application design beyond simple API calls
  • Direct customer engagement and technical evaluation facilitation
  • Clear communication of technical tradeoffs to developer audiences
  • Production deployment and operations experience
  • Bias toward action and ownership mentality
  • LangChain/LangGraph, LLM evaluation, observability, and cloud environments (nice-to-have)
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LangChain

LangChain builds platforms and frameworks for developing, deploying, and observing production AI agents at enterprise scale, including LangSmith for AI observability and evaluation. The company is hiring Deployed Engineers to work directly with enterprise customers on agent implementation and operations, as well as Fullstack Engineers to build features across its platform stack.

Website
langchain.com
View all jobs at LangChain

Likely interview questions

  • Walk us through a production AI agent or LLM application you've built. What were the biggest operational challenges you faced, and how did you solve them?
  • Describe your experience designing multi-step agent workflows. How do you handle orchestration, failure modes, and retries in production?
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