The MLOps Engineer is responsible for the setup, automation, and maintenance of infrastructure and deployment pipelines for AI/ML and microservices-based applications. This role focuses on enabling efficient development, testing, and deployment of AI solutions, including LLM workloads, while ensuring system reliability, scalability, and performance.
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Location: Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates
Contract: 1 year (renewable)
Work Conditions: On-site, Full-time
Salary: $9,800/hr - $15K/hr
Key Responsibilities:
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Infrastructure Support & Environment Management
- Set up and maintain compute infrastructure, including GPU-enabled environments.
- Configure and manage Linux-based systems for development and production environments.
- Monitor system resources and assist in performance tuning and optimization.
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Containerization & Deployment
- Build and manage containerized applications using Docker.
- Deploy and manage applications on Kubernetes clusters.
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CI/CD Pipeline Implementation
- Develop and maintain CI/CD pipelines for application and AI model deployment.
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MLOps & AI Deployment Support
- Deploy machine learning models and LLM-based services.
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Monitoring, Logging & Issue Resolution
- Implement and maintain monitoring and logging solutions.
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Automation & Scripting
- Write scripts (Python, Bash) to automate operational tasks.
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Collaboration & Support
- Work closely with Senior DevOps/MLOps Engineers and AI Engineers.
Required Skills & Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 7+ years of experience in DevOps or platform engineering roles.
- Proficiency in Linux system administration, Docker, and CI/CD tools.
- Scripting skills in Python or Bash.
Preferred Skills:
- Exposure to AI/ML model deployment and MLOps practices.
- Familiarity with LLM deployment concepts and tools.
Language Requirements: Proficiency in English is preferred.
Key Performance Indicators (KPIs):
- Deployment success rate and pipeline stability.
- System uptime and availability.
- Resolution time for incidents and issues.
Reporting Structure:
- Reports to: Senior DevOps / MLOps Engineer / Platform Lead
- Key Stakeholders: AI Engineers, Backend & Frontend Developers, QA & Release Management Teams.
