LangChain
Solutions Engineer (Chicago)
About this role
LangChain seeks a Solutions Engineer in Chicago to be the technical lead in customer engagements, guiding enterprise clients from evaluation through production deployment of AI agents. You'll partner with sales teams to scope POCs, architect real-world solutions, and ensure customer success while providing critical feedback that shapes the product roadmap.
What you'll do
- Own technical discovery, POC design, and architecture reviews for enterprise customers evaluating LangChain
- Co-architect and build production AI agents alongside customer engineering teams from prototype to deployment
- Serve as technical authority in competitive evaluations, security assessments, and infrastructure discussions
- Deliver demos, technical training, and workshops to developer audiences at varying scales
- Advise customers post-sale on architecture decisions, best practices, and identify expansion opportunities
- Create reusable POC assets, example code, and documentation that scale across accounts
What they're looking for
- Solutions engineering or technical sales engineering (6+ years)
- Production AI/LLM deployment experience
- Python or JavaScript for building agents and POCs
- LangChain, LangGraph, or similar agent frameworks
- System architecture and technical evaluation ability
- Clear communication of technical tradeoffs to non-technical audiences
- LLM evaluation, observability, or guardrails experience
- Ability to own outcomes and take responsibility for technical wins
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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
- Tell us about a time you deployed an AI agent or LLM system to production—what was the hardest part and how did you solve it?
- Walk us through how you'd scope a POC with a customer who's never built with AI agents before and is skeptical about the complexity.