LangChain
Deployed Engineer (Boston)
About this role
LangChain seeks a Deployed Engineer to work directly with enterprise customers building production AI agents, bridging technical implementation with go-to-market strategy. You'll co-architect agent systems, lead pre-sales technical evaluations, and help customers deploy and operate AI applications at scale using the LangChain platform.
What you'll do
- Co-architect and build production AI agents with customer engineering teams
- Own technical wins in pre-sales by designing POCs and guiding evaluations
- Deploy and operate agent-based applications including conversational and multi-step workflow systems
- Provide post-sale advisory on architecture, best practices, and product roadmap decisions
- Deliver technical demos, trainings, and workshops for developer audiences
- Surface field feedback and contribute reusable patterns, cookbooks, and example code
What they're looking for
- Python and JavaScript programming
- Agent-based and LLM-powered application design
- Systems architecture and design patterns
- Technical customer engagement and communication
- Cloud environments (AWS, GCP, Azure)
- LLM evaluation and observability tools
- Production software deployment and operations
- Multi-step workflow orchestration
Benefits
- Work on production AI systems with real-world impact
- Fast feedback loops with visible results
- Shape how AI agents are adopted in the field
- Direct influence on product and platform development
- Collaborate with Fortune 500 customers
- Boston-based role with Series B funded company
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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-powered application you've built—what were the hardest parts beyond the initial prototype, and how did you handle failure cases?
- Describe a time you worked directly with a customer or stakeholder to solve a complex technical problem. How did you explain tradeoffs and build trust?