LangChain builds the foundation for agent engineering, helping developers move from prototypes to production-ready AI agents.
Platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, Sandboxes), open source frameworks (LangChain, LangGraph, Deep Agents), and LangSmith Engine.
$125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, Sapphire Ventures.
About the team
Deployed Engineering team works directly with companies building and running AI agents in production.
Hands-on, highly technical team partnering with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work.
Responsibilities
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.
Occasionally contribute code upstream.
Travel to customers up to 25% of the time.
Requirements
6+ 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.
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.
Bias toward action.
Excited about operating AI agents in production.
Nice to have
Deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks.
Worked with LLM evaluation, observability, or guardrails.
Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts.
Shipped and operated production software.
Conditions
Hybrid in office role in Chicago, IL.
Annual OTE range: $200,000–$250,000 USD.
Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.