LangChain
Solutions Engineer (Texas)
About this role
LangChain seeks an experienced Solutions Engineer to drive technical wins for enterprise AI agent deployments. You'll partner with sales teams and customers to design and build production-ready agent systems, from evaluation through rollout, while shaping how the industry adopts AI agent technology.
What you'll do
- Own technical discovery, POC design, and architecture reviews in sales cycles with enterprise customers
- Co-architect and co-build production AI agents with customer engineering teams from prototype to deployment
- Serve as technical authority on agent deployment, infrastructure, security, and competitive evaluations
- Deliver technical trainings, workshops, and demos to developer audiences
- Advise customers post-sale on architecture decisions, best practices, and expansion opportunities
- Surface field feedback to product teams and build reusable POC assets and example code
What they're looking for
- AI agent architecture and deployment
- Solutions engineering or sales engineering
- Technical discovery and POC design
- LangChain, LangGraph, or similar agent frameworks
- Cloud platforms (AWS, GCP, Azure) and Kubernetes
- LLM evaluation and observability tools
- Clear technical communication to developer audiences
- Full-stack software engineering fundamentals
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LangChain
LangChain builds platforms and frameworks for developing, deploying, and observing production AI agents at enterprise scale, including LangSmith for AI observability and evaluation. The company is hiring Deployed Engineers to work directly with enterprise customers on agent implementation and operations, as well as Fullstack Engineers to build features across its platform stack.
- Website
- langchain.com
Likely interview questions
- Walk us through a POC you designed—how did you scope it to match the customer's real use case rather than build a generic demo?
- Describe a time you had to explain a complex technical tradeoff to a non-technical stakeholder and influence their decision. How did you approach it?