LangChain
Sales Engineer (Bay Area)
About this role
LangChain seeks a Sales Engineer to partner with enterprise customers building production AI agents. You'll own the technical aspects of sales engagements, co-architect agent systems, and guide customers through deployment and operation at scale.
What you'll do
- Co-architect and build production AI agents with customer engineering teams
- Design POCs and lead technical evaluations to drive sales wins
- Help customers deploy and operate agent-based applications in production
- Provide post-sale technical advisory on architecture and best practices
- Deliver technical demos, trainings, and workshops for developer audiences
- Travel 40% to customer sites for onboarding and ongoing technical support
What they're looking for
- Python and JavaScript programming
- Agent-based and LLM application design
- Systems architecture and fundamentals
- Customer technical engagement and POC leadership
- Clear communication of technical tradeoffs
- Production AI/LLM deployment experience
- LangChain, LangGraph, or similar framework expertise
- Problem-solving and bias toward action
Benefits
- Work on cutting-edge applied AI problems with Fortune 500 customers
- Direct impact on platform development and real-world adoption
- Hands-on technical role at the intersection of engineering and go-to-market
- Fast feedback loops and visible impact on customer outcomes
- Team environment focused on shipping production-ready solutions
- Opportunity to shape how AI agents are built industry-wide
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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 production AI agent or LLM application you've built—what were the key architectural decisions, and how did you handle failures or edge cases?
- Describe a time you had to explain a complex technical tradeoff to a non-technical stakeholder or customer. How did you approach it?