Director/AI Network Development & Content Delivery

Responsible for the end-to-end design, development, and optimization of network architecture supporting AI workloads, global content provider connectivity, and hyperscaler integration. The role ensures the operator’s backbone, data centres, and peering ecosystems are AI-ready, delivering high-capacity, low-latency, resilient, and cost-efficient interconnection across national and international domains. This enables next-generation digital content delivery, data localization, and hyperscaler services. The candidate will lead infrastructure evolution, connecting UAE AI data centres, and will drive the adoption of advanced AI technologies and GPU clusters within the network.

Responsibilities

• Manage and lead the vision for AI-driven network, content and hyperscaler connectivity design and strategies, infrastructure evolution, ensuring readiness for future digital and data demands.​
• Manages and directs the efficiency of work requirements to ensure the state-of-the-art formulation and implementation of short and long-term projects for AI network developments, content delivery, and hyperscaler connectivity, respective platforms and systems, and services to meet the requirements of AI demands.​
• Manage and oversee network design, capacity, and architecture to ensure KPIs are met, enhance experience, and mitigate network failures.​
• Capacity planning, forecasting, and expansions for the IP/RON network and routing to support exponential AI data growth.​
• Develop a multi-layer architecture connecting GPU clusters, AI traffic framework, AI data centers, AI traffic domains and segmentation models, and edge nodes through high-capacity IP/Optical fabrics.​
• Develop technical frameworks for hosting CDNs, cache nodes, and edge POP nodes and peering models.​
• Drive evolution towards AI-native network designs using 400G/800G transport and ultra-low-latency routing.​
• Ensure the edge infrastructure integrates seamlessly with core AI compute and hyperscaler zones.​
• Define KPIs for latency, throughput, and availability to optimize and enhance traffic localization ratios to reduce upstream transit cost.​
• Develop policies for dynamic bandwidth allocation between AI, CDN, and hyperscaler workloads and connectivity. In addition, define routing policies, redundancy, and scaling models.​
• Ensure compliance with IPv6, RPKI, and secure BGP practices for all hyperscaler interconnections.​
• Coordinate with content and peering teams to localize AI data sources and model distribution points.​
• Engage with IP, transport, data center, and hyperscaler teams to unify AI and non-AI infrastructure planning for customer network.​
• Engage with global AI cloud providers to ensure alignment with interconnect standards, best practices, and topology designs.​
• Act as the technical lead for partnerships with global content and hyperscaler partners.​
• Ensure all projects are performed with consistency, reliability, efficiency, and financial compliance with engineering and customer standards, objectives, priorities, guidelines, and procedures.

Qualifications

• Minimum 10 years of experience in telecom network development, hyperscaler integration, CDNs, and content hosting.
• At least 3 years of experience in AI-centric or high-performance networking environments.
• Strong background in AI infrastructure development, including GPU cluster integration and AI workload optimization.
• Proven project management experience, with a track record of delivering large-scale, multi-region network projects.
• In-depth knowledge of IP/MPLS, transport and access networks, traffic engineering, hyperscaler and cloud technologies.
• Experience with submarine cable systems, global connectivity strategies, and latency-sensitive, secure infrastructure design.
• Familiarity with global AI data centre standards and connectivity requirements.
• Excellent collaboration and communication skills, with the ability to lead cross-functional teams and technical partnerships.
• Strong planning, budgeting, and forecasting skills.