Роль четко определена с акцентом на приложения LLM, а диапазон зарплаты привлекателен для рынка. Однако требование о конкретном местоположении может ограничить некоторых потенциальных кандидатов.
Высокая зарплата
Стартап-среда
Четкие обязанности
Требование по местоположению
Мэтч & Сопровод
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Описание вакансии
Текст:
Hirify AI/Оригинал
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.
Будьте осторожны
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