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7 дней назад

AI/ML Engineer (AIOps)

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

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TL;DR

AI/ML Engineer (AIOps): Building and architecting scalable AI-driven automation solutions for IT operations with an accent on LLM integration, anomaly detection, and predictive maintenance. Focus on developing AI agents, fine-tuning open-source models, and optimizing network diagnostics within complex IT environments.

Location: Petaling Jaya, Selangor, Malaysia

hirify.global

hirify.global is a global technology services company specializing in IT infrastructure and automation solutions.

What you will do

  • Design and architect scalable AI solutions for automated network diagnostics and NOC operations.
  • Lead the evaluation, fine-tuning, and deployment of open-source and proprietary LLMs.
  • Develop and own automation frameworks using Python and PowerShell.
  • Train machine learning models for anomaly detection, predictive maintenance, and capacity planning.
  • Act as a Solution Owner in an Agile environment, managing backlogs and defining technical requirements.
  • Collaborate with cross-functional teams to validate solutions and ensure product-market fit.

Requirements

  • 4+ years of experience in IT services with 3+ years in IT service automation and AI engineering.
  • Proficiency in Python, PowerShell, or Shell scripting.
  • Hands-on experience with LLMs (Llama, GPT-4) and deep learning frameworks like PyTorch.
  • Experience with containerization (Docker, Kubernetes) and cloud platforms (Azure or GCP).
  • Strong understanding of network topologies, protocols, and ITIL processes.
  • Excellent analytical, problem-solving, and communication skills.

Culture & Benefits

  • Opportunity to work on cutting-edge GenAI and AIOps solutions.
  • Collaborative environment focused on continuous service improvement.
  • Professional development and knowledge sharing within the organization.
  • Exposure to large-scale IT operational data and complex infrastructure challenges.

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