Langchain Deployed Engineer at Langchain partners with customers to co-architect and build production AI agents, ensuring reliable deployment and operation. The role supports customers from pre-sales to post-deployment advisory.
Responsibilities
You'll work on some of the hardest problems in applied AI alongside customers. You will help customers adopt innovative new practices in agentic engineering, and ensure they ship reliable agents into production, quickly. The feedback loop is fast, the impact is visible, and your work directly shapes how AI agents are built in the real world.
Co-architect and co-build production AI agents with customer engineering teams and customers
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
Build and run tailored demos, trainings, and workshops for developer audiences
Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
Interface with LangChain's engineering, product and design (EPD) team to share field perspectives and guide product direction based on customer priorities
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
1-3 years of experience in software engineering, customer engineering, solutions engineering, founding engineering, or a similarly technical role, ideally at a startup or high-growth company
Qualification
You’ve deployed AI agents in productionWorked with LLM evaluation
Preferred
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 Kubernetes concepts
Have shipped and operated production software and are comfortable owning systems under real-world constraints
