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Deployed Engineer (AI)

Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
Deployed Engineer (AI): Co-architecting and co-building production AI agents with customer engineering teams, with an accent on agent architectures, technical evaluations, and reliable operation at scale. Focus on designing multi-step LLM workflows, handling production failure modes, and solving deployment, observability, and evaluation challenges for real-world systems.

Location: New York, NY; on-site

Annual OTE range: $185,000–$315,000 USD

Company

LangChain builds open-source frameworks and a platform for building, evaluating, deploying, and operating AI agents at scale, including LangSmith, LangChain, LangGraph, Deep Agents, and LangSmith Engine.

What you will do

  • Co-architect and co-build production AI agents with customer engineering teams.
  • Own the technical win in pre-sales by designing proofs of concept, answering technical questions, and guiding evaluations.
  • Help customers deploy and operate conversational agents, research agents, and multi-step agent workflows.
  • Advise customers on architecture, best practices, and roadmap-level decisions after deployment.
  • Run technical demos, trainings, and workshops for developer audiences.
  • Capture field feedback and contribute reusable patterns, cookbooks, and example code.

Requirements

  • 3+ years of experience in software engineering, customer engineering, solutions engineering, founding engineering, or product engineering.
  • Strong Python, JavaScript, and systems fundamentals.
  • Experience designing agent-based or LLM-powered applications with multi-step workflows, orchestration, and failure handling.
  • Comfort working directly with customers during proofs of concept, architecture reviews, and technical evaluations.
  • Experience deploying AI agents in production, especially with LangChain, LangGraph, or similar frameworks.
  • Experience with LLM evaluation, observability, guardrails, cloud environments, containers, and basic Kubernetes concepts.

Nice to have

  • Experience in a startup or scale-up.
  • Background in customer-facing technical work and developer education.

Culture & Benefits

  • Hands-on work on production AI systems used by real teams.
  • Close collaboration across engineering, product, and go-to-market.
  • Medical, dental, and vision coverage.
  • Flexible vacation and a 401(k) plan.
  • Meals on in-office days in the US.

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