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5 дней назад

LLM Application Engineer (AI)

Формат работы
hybrid
Тип работы
fulltime
Английский
b2
Страна
SK
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
LLM Application Engineer (AI) (LLMs/Agentic Workflows): Build and ship LLM-powered applications and agent workflows that turn AI capabilities into reliable user experiences with an accent on reasoning, persistent context, tool use, and production orchestration. Focus on designing evaluation systems, improving model behavior, and building observable, predictable, and cost-effective AI workflows across models, backend services, and product UX.

Location: Hybrid in Seoul, Korea

Company

hirify.global's A1 initiative builds a proactive smart assistant for everyday conversations, errands, organization, and workflows.

What you will do

  • Build and ship LLM-powered applications and AI agent workflows.
  • Design reasoning, planning, memory, tool-calling, and multi-step execution systems.
  • Integrate LLMs with APIs, databases, search, internal services, and external tools.
  • Build orchestration pipelines that make probabilistic model outputs predictable, observable, and safe.
  • Develop evaluation frameworks and datasets to measure AI quality, reliability, and regressions.
  • Optimize AI systems for quality, latency, and cost while collaborating with product and engineering teams.

Requirements

  • Strong software engineering fundamentals and experience building AI-powered applications.
  • Hands-on experience with LLMs, generative AI, or agent-based systems.
  • Experience designing prompts, workflows, evaluations, or AI behavior.
  • Ability to write clean, production-quality Python code.
  • Comfort working across model, system, and product abstraction layers.
  • Strong problem-solving skills in ambiguous, fast-moving environments with a bias toward shipping and iteration.

Culture & Benefits

  • Work in a hybrid setup based in Seoul, Korea.
  • Own AI problems end-to-end, from user needs through production delivery.
  • Collaborate closely with product and engineering teams.
  • Build systems focused on reliability, observability, maintainability, and measurable user impact.

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