Solutions Engineer

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 Team This team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Sales Engineers sit at the intersection of engineering, product, and go-to-market , shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. About the Role You'll work on some of the hardest problems in applied AI alongside customers. This is not demos or research, but helping teams build systems they rely on in production. The feedback loop is fast, the impact is visible, and your work directly shapes how AI agents are built in the real world. What You’ll Do Co-architect and co-build production AI agents with customer engineering teams Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows Advise customers post-sale on architecture, best practices, and roadmap-level decisions Run technical demos, trainings, and workshops for developer audiences Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers Occasionally contribute code upstream when it meaningfully improves customer outcomes This role requires 40% travel to customer sites to support deployment, onboarding, and ongoing technical engagement What You’ll Bring 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up Strong Python, JavaScript and systems fundamentals Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations Can explain technical tradeoffs clearly and build trust with developer audiences Take responsibility for outcomes , not just recommendations Have a bias toward action and enjoy figuring things out as you go Are excited about operating AI agents in production , not just building demos Nice to Have’s: You’ve deployed AI agents in production , especially using LangChain, LangGraph , or similar frameworks Worked with LLM evaluation, observability, or guardrails Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts Have shipped and operated production software and are comfortable owning systems under real-world constraints #J-18808-Ljbffr