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LangChain

Deployed Engineer (Bay Area)

San Francisco, CAfulltimemidAdded 1 month ago

About this role

LangChain seeks a Deployed Engineer to work hands-on with enterprise customers building production AI agents. You'll co-architect solutions, own technical evaluations, and guide customers from POC through deployment and ongoing operations, with 40% travel to customer sites.

What you'll do

  • Co-architect and build production AI agents with customer engineering teams
  • Own technical wins in pre-sales by designing POCs and guiding evaluations
  • Deploy and operate agent-based applications including conversational and research agents
  • Advise customers on architecture, best practices, and roadmap decisions post-sale
  • Run technical demos, trainings, and workshops for developer audiences
  • Surface field feedback and contribute reusable patterns and example code

What they're looking for

  • Python and JavaScript programming
  • Agent and LLM-powered application design
  • Systems architecture and fundamentals
  • Direct customer engagement and technical sales
  • Technical communication and explanation of tradeoffs
  • Multi-step workflow orchestration and failure handling
  • LangChain or LangGraph frameworks (nice to have)
  • LLM evaluation and observability (nice to have)

Benefits

  • Work on production AI agent problems with major enterprises
  • Fast feedback loop with visible, direct impact
  • Shape how AI agents are built in the real world
  • Opportunity to influence product via field insights
  • Startup environment with Series B funding ($125M)
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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.

View all jobs at LangChain

Likely interview questions

  • Walk us through a production AI agent or LLM application you've designed—what was the architecture, what challenges did you hit, and how did you handle failure cases?
  • Tell us about a time you worked directly with a customer or end user to solve a technical problem. How did you approach the engagement and what was the outcome?