Deployed Engineer, Professional Services (NYC)
AI summary of the role
Deployed Engineer on the Professional Services team, working directly with enterprise customers to design, co-build, and embed within teams to ship production-grade AI agents.
What you’ll do
- Advise on agent architecture design, evaluation strategy, and production best practices.
- Co-build with customer engineering teams across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements.
- Embed as a deployed engineer inside customer teams for extended engagements, shipping agent systems directly.
- Own agent engineering end-to-end: architecture, orchestration, evals, custom conversational UIs, and production deployment.
What you’ll bring
- 4+ years of software engineering experience with deep expertise in Python.
- 2+ years of hands-on experience building and shipping production agent systems.
- Strong client-facing communication skills with technical stakeholders (engineers, architects, CTOs).
- Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management.
Technologies
Python · TypeScript · JavaScript · LangChain · LangGraph · Deep Agents · Axolotl · Unsloth · Hugging Face transformers · TRL
About LangChain
Open-source and commercial platform for building, evaluating, deploying, and operating AI agents at scale.
Series B
Source and classification
Implementation & delivery · Evidence for this classification:
(LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. About the Role We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embeddedHow jobs are selected
Employer postings · Data from · Sources