AI Engineer, Enablement

Today, LangChain, LangGraph, LangSmith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500 .

With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures , we're at a stage where we're continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.

The Enablement team works directly with companies building AI agents for production, getting new LangChain customers off to a fast, confident start. We own onboarding and instructor-led education for our customers, and run focused advisory sprints for accounts that need deeper support on architecture and evaluation.

You'll set the technical foundation for every new customer - teaching their teams to build effectively on the platform and advising on agent development and evaluation as they go.

You are someone who's built real agent systems, can defend the tradeoffs in them, and genuinely loves teaching, whether that's a live workshop for 50 engineers or a 1:1 debugging session with one stuck developer. You'll also build the internal agents and tools that make the Enablement team itself more efficient.

- Own onboarding and education for new enterprise customers to get them building effectively on the platform, fast

- Design and run live, hands-on workshops that build real product fluency, not just familiarity

- Run focused, time-boxed advisory sprints for customers working through architecture or evaluation challenges

- Build internal agents and tools that streamline how the Enablement team operates - automating our processes so the team scales without just adding headcount

- Create enablement assets (tutorials, reference implementations, best-practice guides) that scale beyond 1:1 time

- Act as the voice of the new customer inside LangChain, feeding friction points back to Product and Engineering

- Stay current on agent engineering practice and fold what you learn into what you teach

- 3+ years building LLM/agent applications - you've designed real agent architectures and evaluation strategies, not just wired up an API call

- Strong Python, comfortable writing and debugging code live, in front of a customer

- 2+ years in a technical, customer-facing role (Enablement, Customer Success Engineering, Solutions Engineering, or similar), including experience designing and delivering live workshops

- Genuine enjoyment of teaching - you'd rather leave a customer more capable than impressed

- Can take a complex technical concept and land it with both an individual developer and a room of enterprise stakeholders

- Comfortable operating independently in ambiguity, managing several customer engagements at once

- You've deployed AI agents in production , especially using LangChain, LangGraph , or similar frameworks

- Hands-on experience with LangSmith (evals, tracing, observability)

- Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts