Partner Engineer

LangChain

6d•$170,000 - $200,000

About The Position

LangChain is seeking a Partner Engineer to build the technical foundation for its ecosystem of Cloud providers and Systems Integrators. This role is crucial for enabling partner motions, including co-selling with cloud hyperscalers and empowering systems integrators (SIs) and global systems integrators (GSIs) to deliver LangChain solutions independently. The Partner Engineer will create reference architectures, integration patterns, and enablement programs that allow partners to take LangChain's technology to their clients. This position offers a unique opportunity to shape the real-world application of AI agents and contribute to a rapidly growing company with significant funding and a strong market presence.

Requirements

7+ years of experience in a technical, partner-facing or customer-facing role such as Partner Engineer, Solutions Architect, or Sales Engineer.
Track record of partnering with systems integrators, cloud providers, or technology and ISV partners to solve complex technical problems, from initial solution design through delivery.
Experience building enablement programs, such as certifications, workshops, or delivery playbooks, that let a partner's technical team operate independently instead of depending on you for every deal.
Comfortable operating across engineering, partnerships, and sales at the same time.
Strong presentation and technical communication skills, able to translate between technical and business stakeholders, including C-suite executives.
Hands-on experience building with LLM frameworks such as langchain, langgraph, or similar, including RAG patterns and agent architecture.
Experience designing evaluation frameworks and iterating on prompts and agent behavior based on results.
Strong Python skills.
Working knowledge of GCP and/or Azure cloud services.
Experience designing integrations, sample repositories, or reference architectures that other teams can pick up and reuse.
Familiarity with vector stores, tool integration, and API design in agentic systems.

Nice To Haves

Former founders, and technical leaders with unusual backgrounds but the right skill set are welcome to apply.

Responsibilities

Build reference architectures and integration patterns that support the technical side of co-sell, from marketplace listings to joint solution briefs.
Partner with cloud field and partner teams on solutioning for named accounts.
Keep marketplace presence and co-sell technical assets current as LangChain, LangGraph, and LangSmith evolve.
Build and run LangChain's services partner enablement program from the ground up, including tiered certification tracks, hands-on workshops, delivery playbooks, and implementation guides based on our agent development lifecycle (ADLC) framework.
Run LangChain's partner certification program in the region: deliver certification workshops, review certification projects, and guide partner engineers through the program.
Work with SI centers of excellence (CoEs) to develop repeatable joint solutions and reference architectures, so an SI's own teams can scope, build, and deploy agentic AI independently instead of pulling in LangChain engineering for every deal.
Act as a technical evangelist in the field: work directly with an SI's client account teams to make the technical case for LangChain on a specific engagement.
Build and maintain joint business plans with strategic services partners, translating account and industry priorities into a shared technical roadmap and a concrete set of reference assets.
Help SI teams grow from an individual technical champion into a full center of excellence, then support that expertise as it spreads across their client base.
Contribute technical enablement to our Tier 1 GSI strategy as it matures, including scoping what a global systems integrator needs to stand up its own agentic AI practice.
Serve as a technical escalation point for complex partner-led deployments.
Represent LangChain at partner events, summits, and joint webinars.
Gather feedback from partners and the field, and route it back to product and engineering.