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Salary: USD 150,000 - 195,000 / annual
LangChain is seeking an AI Engineer for its Enablement team to set the technical foundation for how customers learn to build reliable agents with the LangChain ecosystem. You'll design and deliver instructor-led workshops, create scalable enablement assets (tutorials, reference implementations, best-practice guides), and provide technical guidance through office hours. The role bridges product and customer success, requiring you to build internal agents and tools that streamline team operations while feeding customer friction points back to Product and Engineering.
You'll work with a diverse customer base—from individual developers to enterprise teams at Fortune 500 companies like Klarna, Coinbase, and Workday—teaching them to work effectively with LangChain, LangGraph, Deep Agents, and LangSmith. This is a hands-on position where you'll write and debug code live in front of customers, design agent architectures, and stay current on evolving agent engineering practices.
The ideal candidate has 3+ years building LLM/agent applications with strong Python skills and 2+ years in a technical customer-facing role (Enablement, Customer Success Engineering, Solutions Engineering). You're someone who genuinely loves teaching, can defend architectural tradeoffs, and prefers leaving customers more capable than impressed. You'll need exceptional presentation skills, the ability to explain complex concepts to diverse audiences, and comfort operating independently while managing multiple customer engagements. Production deployment experience with LangChain or similar frameworks, hands-on LLM evaluation/observability experience, and familiarity with cloud environments (AWS, GCP, Azure) and Kubernetes are nice-to-haves. The role requires up to 20% travel.
About LangChain
AI / Data / Infrastructure — developer platform and framework for building LLM applications and agents.
