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4 дня назад

GenAI Engineer (AI)

Формат работы
remote (только USA)
Тип работы
fulltime
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

GenAI Engineer (AI): Design, implement, fine-tune, and deploy generative AI and ML solutions for real client use cases with an accent on scalable architectures, model selection, and production readiness in regulated environments. Focus on end-to-end delivery, MLOps practices, performance tradeoffs, and clear communication to stakeholders.

Location: Remote from anywhere within the US

Company

hirify.global is a remote-first organization building applied AI capabilities for client delivery.

What you will do

  • Design, implement, fine-tune, and deploy GenAI and ML solutions aligned to business problems.
  • Translate requirements into scalable AI architectures and lead end-to-end delivery.
  • Evaluate models like GPT-based LLMs, handle tuning, integration, and scaling decisions.
  • Collaborate with Value Engineers, clients, and stakeholders to align solutions with goals.
  • Contribute to MLOps, knowledge sharing, mentoring, and internal AI best practices.
  • Stay current on GenAI trends and experiment with new techniques.

Requirements

  • Bachelor’s or Master’s in Computer Science, AI, ML, or related field
  • Experience delivering technical projects with cross-stakeholder collaboration
  • Hands-on with generative AI (LLMs, fine-tuning, embeddings)
  • Experience deploying models on cloud platforms (AWS, Azure, GCP)
  • Strong communication to explain technical concepts to non-technical audiences
  • Ownership, priority management in fast-paced environments

Nice to have

  • MLOps, containerization, model monitoring
  • AI/NLP applications beyond experimentation
  • AI evaluation methods and metrics
  • Ethical AI, governance, data security
  • Regulated industries experience

Culture & Benefits

  • Remote-first work environment
  • Emphasis on ownership, delivery excellence, and continuous learning
  • Collaborative teamwork with knowledge sharing and mentoring
  • Client-focused mindset with emphasis on outcomes and trust-building
  • Support for innovation in AI delivery methods and processes

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