LangChain
Deployed Engineer (Raleigh)
About this role
LangChain seeks a Deployed Engineer to partner with enterprise customers building production AI agents. You'll co-architect and operate agent-based systems, own technical evaluations, and translate field insights back into the platform—working at the intersection of engineering, product, and customer success.
What you'll do
- Co-architect and build production AI agents alongside customer engineering teams
- Lead technical evaluations and POCs to drive pre-sales wins
- Deploy and operate agent applications including conversational and multi-step workflow systems
- Advise customers on architecture, best practices, and product roadmap decisions
- Deliver technical demos, trainings, and workshops for developer audiences
- Generate reusable patterns, cookbooks, and example code based on field feedback
What they're looking for
- Python and JavaScript programming
- Agent-based and LLM-powered application design
- Systems design and architecture
- Customer technical engagement and communication
- Multi-step workflow orchestration and failure handling
- Cloud platforms (AWS, GCP, Azure)
- LangChain/LangGraph frameworks (nice to have)
- Production software deployment and operations
Benefits
- Work on production AI systems with real-world impact
- Fast feedback loops and visible results
- Direct influence on platform development
- Hands-on technical collaboration with Fortune 500 companies
- Startup environment with meaningful individual contributions
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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 designed and deployed an LLM-powered or agent-based application in production. What were the biggest technical challenges, and how did you handle them?
- Walk us through how you would architect a multi-step agent workflow that needs to be reliable and observable in production. What tradeoffs would you consider?