About the job
About LangChain
LangChain is on a mission to make intelligent agents a fundamental aspect of technology. We provide a robust framework for agent engineering, enabling developers to transition from prototypes to production-ready AI agents. Originally launched as a set of widely utilized open-source tools, we have expanded our offerings to include a comprehensive platform for building, evaluating, deploying, and managing AI agents at scale.
Our products, including LangChain, LangGraph, LangSmith, and Agent Builder, are trusted by teams developing real AI solutions at leading companies, such as Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500.
With $125M raised in our Series B funding round from prominent investors like IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we are focused on continuous product innovation and growth. Every team member at LangChain can make a significant impact on our technology and collaborative efforts.
About the Role
We are seeking a Senior Technical Support Engineer to enhance the technical support experience for our advanced users, ranging from AI engineers to infrastructure architects. You will play a crucial role in assisting teams with debugging production LLM applications and agents, enhancing AI observability, and resolving critical challenges. Additionally, you will help define the standards for exceptional technical support in the context of modern AI platforms.
Key Responsibilities
Serve as the primary escalation point for technical support inquiries, diagnosing issues related to customer setups, LangChain products, and deployment challenges.
Collaborate closely with engineering, operations, documentation, and product teams to address bugs, propose solutions, and champion customer needs.
Partner with Deployed Engineering to support key enterprise clients.
Develop and enhance internal tools, diagnostic protocols, and runbooks for complex issue resolution.
Lead post-mortem analyses for significant incidents and integrate findings back into product development and documentation.
Refine key performance indicators (KPIs) for support effectiveness (MTTR, CSAT, bug recurrence) and drive ongoing improvements.
Enhance documentation and knowledge base articles to support user education.

