LangChain
Deployed Engineer, Professional Services (NYC)
About this role
LangChain is hiring a Deployed Engineer for Professional Services 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 using LangChain's frameworks.
What you'll do
- Design agent architectures and guide enterprise customers through production deployment
- Co-build evaluation pipelines and agent systems alongside customer engineering teams
- Serve as an embedded engineer within customer organizations for extended engagements
- Implement end-to-end agent development lifecycle including orchestration, evals, and custom UIs
- Apply post-training and fine-tuning techniques to optimize agent performance
- Communicate technical tradeoffs and architectural decisions to CTOs and senior engineers
What they're looking for
- Python (4+ years production experience)
- LangChain/LangGraph/Deep Agents frameworks
- Production agent systems architecture and deployment
- Multi-agent patterns and state management
- Evaluation methodology design for AI systems
- TypeScript/JavaScript (preferred)
- Post-training techniques (SFT, DPO, RLHF)
- Trace mining and continuous improvement
Benefits
- Medical, dental, and vision coverage
- Flexible vacation policy
- 401(k) plan
- Meals provided on in-office days
- Meaningful equity
- Competitive base salary with variable compensation
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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 agent system you've built end-to-end—what were the key architectural decisions and tradeoffs?
- How would you approach designing an evaluation strategy for a non-deterministic AI system when the customer's success metrics are vague?