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LangChainLangChainSeattle, WA

Deployed Engineer (Seattle)

Deployed Engineer co-architects and builds production AI agents with customers, owns pre-sales technical wins via POCs and evaluations, and advises on deployment and operations. Requires 5+ years experience, strong Python/JavaScript, and expertise in agent-based LLM applications.

165k – 280k
On-site5+ YOESolutions Architecture

About the role

What You’ll Do

  • Co-architect and co-build production AI agents with customer engineering teams
  • Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
  • Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
  • Advise customers post-sale on architecture, best practices, and roadmap-level decisions
  • Run technical demos, trainings, and workshops for developer audiences
  • Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
  • Occasionally contribute code upstream when it meaningfully improves customer outcomes

What You’ll Bring

  • 5+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
  • Strong Python, JavaScript and systems fundamentals
  • Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
  • Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
  • Can explain technical tradeoffs clearly and build trust with developer audiences
  • Take responsibility for outcomes, not just recommendations
  • Have a bias toward action and enjoy figuring things out as you go
  • Are excited about operating AI agents in production, not just building demos

Nice to Have’s

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

Compensation

Annual OTE range: $165,000–$280,000 USD

Skills

PythonJavaScriptLangChainLangGraphLLMsAWSGCPAzureKubernetesLlm EvaluationObservabilityGuardrails

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