Site Reliability Engineer

Job description
Location: Dubai
Duration: Permanent
We are currently partnered with a leading technology consultancy who are scaling their tech team. They offer a diverse work environment that provides services in the UAE impacting millions of lives. We're currently helping them search for a Site Reliability Engineer to join their ever-growing team.
Responsibilities:

1. Architect, implement, and oversee scalable, high-performance AI and data infrastructure across cloud (AWS) and on-prem environments.
2. Utilize automation tools (e.g., Terraform, Ansible) for provisioning, monitoring, and infrastructure optimization.
3. Design robust monitoring, alerting, and logging solutions to detect and mitigate potential failures before they impact operations.
4. Develop and maintain seamless CI/CD pipelines to accelerate the deployment of AI models and data-driven applications.
5. Optimize workflows to enhance efficiency, reduce deployment friction, and maintain system stability.
6. Partner with AI researchers, data engineers, and developers to align infrastructure with project needs.
7. Act as a bridge between AI, data, and infrastructure teams, ensuring smooth communication and technical alignment.
8. Rapidly diagnose and resolve system incidents, conducting thorough root-cause analyses to prevent future issues.
9. Establish and refine disaster recovery frameworks to safeguard AI and data assets.
10. Implement stringent security protocols to protect AI and data infrastructure, ensuring compliance with industry regulations.
11. Perform regular security evaluations, proactively addressing vulnerabilities.
12. Identify opportunities to improve system scalability, efficiency, and resilience.
13. Stay ahead of emerging trends in AI infrastructure, site reliability engineering, and cloud technologies.

1. Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
2. 3-5 years experience in a similar role.
3. Experience with on-premise and cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).
4. Experience with AI and data-specific infrastructure (e.g., GPU clusters, data lakes).
5. Understanding of machine learning frameworks and data processing tools (e.g., TensorFlow, PyTorch, Apache Spark).