LangChain
Deployed Engineer, Professional Services
About this role
LangChain seeks a Deployed Engineer to work with enterprise customers on building production-ready AI agents. You'll advise on architecture, co-build solutions, and embed within customer teams to ship agent systems end-to-end, leveraging deep expertise in Python, LangChain frameworks, and production AI systems.
What you'll do
- Design agent architectures and review evaluation strategies for enterprise customers
- Co-build agent systems across the full development lifecycle with customer engineering teams
- Embed as a deployed engineer within customer organizations for extended engagements
- Guide customers through agent engineering, orchestration patterns, evals, and production deployment
- Apply post-training techniques, model selection, and evaluation methodology for AI systems
- Communicate technical tradeoffs and architectural decisions to engineering leadership
What they're looking for
- Python (4+ years experience)
- TypeScript/JavaScript
- LangChain, LangGraph, and Deep Agents frameworks
- Production agent system design and shipping
- Evaluation methodology for non-deterministic AI systems
- Multi-agent patterns and state management
- Client-facing technical communication
- Post-training techniques (SFT, DPO, RLHF)
Benefits
- Medical, dental, and vision coverage
- Flexible vacation
- 401(k) plan
- Meals on in-office days (US)
- Meaningful equity
- Competitive base salary
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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
- Describe a production agent system you built end-to-end—what architecture did you choose and why?
- How do you approach designing evaluation methodologies for non-deterministic AI systems, and what challenges have you encountered?