Deployed Engineer (boston)

LangChain LangChain · Data AI · Boston, MA · Deployed Engineering

LangChain is seeking a Deployed Engineer to work directly with companies building and operating AI agents in production. This role involves co-architecting and co-building agent systems, guiding customers through technical wins, deploying and operating agent-based applications, and advising on architecture and best practices. The role sits at the intersection of engineering, product, and go-to-market, with a focus on shipping production-ready AI agents.

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

  • 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering)
  • 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
  • bias toward action
  • enjoy figuring things out as you go

Nice to have

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

What the JD emphasized

  • systems that real teams depend on in production
  • operating AI agents in production
  • agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling

Other signals

  • customer-facing
  • production systems
  • agent engineering
  • scale