Provide end-to-end AI technical leadership and solution architecture ownership across AI Projects.
The role ensures sound architectural decisions, correct application of AI patterns, and delivery-ready designs, while reducing technical dependency on Project Managers and strengthening execution alignment across delivery teams.
Key Responsibilities Architecture & Technical Leadership Own and lead AI solution architecture across projects, from concept design through deployment and operational readiness.
Define and review end-to-end solution architectures , including AI models, data pipelines, platforms, integrations, infrastructure, and security considerations.
Ensure architectural decisions are scalable, secure, cost-effective, and aligned with enterprise and client standards.
AI Pattern & Technology Selection Ensure the correct selection and implementation of AI patterns , including: Computer Vision (CV) Natural Language Processing (NLP) Advanced Analytics Large Language Models (LLMs) Evaluate trade-offs between model types, architectures, and deployment approaches (cloud, on-prem, edge).
Delivery & PM Enablement Support Project Managers and Delivery Managers with: Technical estimations and feasibility assessments Identification of technical risks, dependencies, and constraints Delivery sequencing and technical milestone definition Act as the technical reference point to unblock delivery and reduce escalation cycles.
Platform, Integration & Readiness Drive technical alignment across AI teams, platform teams, and infrastructure teams.
Ensure code readiness, release readiness, and integration planning across environments (Dev, SIT, UAT, Prod).
Review technical deliverables to ensure quality, consistency, and architectural compliance.
Stakeholder Communication Translate complex technical decisions into clear, structured communication for non-technical stakeholders .
Participate in client discussions where architectural clarity or technical assurance is required.
Qualifications & Requirements ·       Strong hands-on background in delivering enterprise-grade AI projects and AI platforms .
·       Proven experience in solution architecture , including AI systems, data architectures, and system integrations.
·       Solid understanding of AI lifecycle, model deployment, monitoring, and operational considerations.
·       Ability to balance technical depth with delivery practicality.
Required Certifications AI / ML certification from Azure, AWS, or GCP (mandatory or strong requirement).
Arabic speaker with strong spoken and written English .
·   Immediate joining preferred.