Position Objective:
To lead AI operations and cloud automation initiatives for private cloud infrastructure, developing and managing an AIOps framework, defining use cases, tools, and platforms, and building an AI adoption roadmap to drive business value realization.
Job Description and Responsibilities:
- Leads the development and implementation of an AIOps framework for private cloud infrastructure, integrating AI-powered observability and automation.
- Defines AIOps use cases and calculates business value realization to align with strategic goals.
- Develops and maintains a comprehensive AI adoption roadmap for cloud-native Telco/IT infrastructure.
- Manages deployment and integration of AIOps tools (e.g., monitoring systems, service assurance platforms) with existing infrastructure.
- Collaborates with cross-functional teams to build closed-loop automation and self-healing capabilities for L1/L2 tasks.
- Designs, tests, and programs automation equipment and processes to enhance operational efficiency.
- Ensures AI governance, model explainability, and effective use of AI in network operations.
- Supervises automation processes to maintain performance and quality standards.
- Identifies and resolves quality issues and defects in AIOps and automation solutions, writing detailed reports.
- Monitors advancements in AIOps and cloud technologies (e.g., AWS, Azure, Google Cloud) to maintain a state-of-the-art implementation.
Qualifications and Experience:
- Minimum 10 years of experience in cloud-native operations or telecom network automation, including 3 years in AI Ops or cloud automation roles.
- Bachelor’s or master’s degree in computer science, Data Science, or a related field from a reputed university.
- Techno-functional role requiring expertise in AIOps, machine learning engineering, and applied AI for IT/network operations (e.g., observability, automation, service assurance).
- Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for Telco workloads.
- Strong AI Ops and cloud automation skills, including AI/ML pipeline development, automation scripting (e.g., Python, Ansible), observability design, and incident management automation.
- Ability to lead technical and functional aspects of AI Ops initiatives, balancing AI/ML expertise with process automation and cross-functional collaboration.
- Excellent communication (written, oral, listening), leadership, negotiation, presentation, coaching, and mentoring skills.
- Industry-recognized certifications in AIOps, cloud, or AI technologies (e.g., AWS Certified Machine Learning, Google Cloud Professional Machine Learning Engineer, ITIL) are desirable.
- Strong command of both English and Arabic languages (both written and spoken).