Architect Customer Agents: Design and deploy production-grade agents that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows.
Drive Case Deflection: Analyze customer friction points and build self-service AI systems that significantly reduce support volume while improving the quality of the customer experience.
Own the Domain: Act as the product owner and the technical muscle proactively identifying opportunities for improvement, propose architectures, and own the full lifecycle of the systems you build.
Dogfood the Stack: Be a key member of the feedback loop for our product team, identifying gaps in our frameworks and contributing back to the LangChain and LangGraph open-source ecosystem.
Build Onboarding Workflows: Develop "AI-native onboarding" experiences that help enterprise customers move from prototypes to production faster by automating documentation retrieval and code generation.

deep understanding of the LLM stack: prompting, retrieval (RAG), cognitive architectures, and agentic loops
strong software engineer (typically 3+ years) with at least 1 year of experience specifically shipping LLM systems in production
comfortable navigating ambiguity, identifying high-impact problems, and driving them to completion autonomously
strong coding skills in Python or TypeScript (ideally both) with the ability to build end-to-end applications
enjoy the intersection of high-level engineering and direct customer impact, translating a customer's technical pain point into scalable system architecture
expertise with LangSmith for evaluation and monitoring (nice to have)
experience building or maintaining open-source projects (nice to have)
background in technical consulting, solutions engineering, or high-growth GTM teams (nice to have)

GTM Engineer

Key skills