About Us

At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.

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.

Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.

About the Role

We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embedded inside their team shipping alongside their engineers. You are someone who's built real AI systems for production and can defend the technical tradeoffs within them.

Key Responsibilities

• Advising: Agent architecture design, evaluation strategy review, and best-practice production guidance.
• Building: Co-build with the customer's engineering team across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements.
• Embedding: Serve as a deployed engineer inside the customer's team for extended engagements, operating as a de facto member of their org to ship agent systems directly.
• Agent Engineering: ADLC end-to-end, architecture design, orchestration patterns, evals, custom conversational UIs, and production deployment.
• Applied AI: Post-training, supervised fine-tuning, harness engineering, trace mining, model selection and evaluation methodology.