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
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Own and lead AI solution architecture across projects, from concept design through deployment and operational readiness.
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Define and review end-to-end solution architectures, including AI models, data pipelines, platforms, integrations, infrastructure, and security considerations.
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Ensure architectural decisions are scalable, secure, cost-effective, and aligned with enterprise and client standards.
AI Pattern & Technology Selection
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Ensure the correct selection and implementation of AI patterns, including:
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Computer Vision (CV)
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Natural Language Processing (NLP)
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Advanced Analytics
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Large Language Models (LLMs)
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Evaluate trade-offs between model types, architectures, and deployment approaches (cloud, on-prem, edge).
Delivery & PM Enablement
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Support Project Managers and Delivery Managers with:
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Technical estimations and feasibility assessments
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Identification of technical risks, dependencies, and constraints
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Delivery sequencing and technical milestone definition
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Act as the technical reference point to unblock delivery and reduce escalation cycles.
Platform, Integration & Readiness
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Drive technical alignment across AI teams, platform teams, and infrastructure teams.
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Ensure code readiness, release readiness, and integration planning across environments (Dev, SIT, UAT, Prod).
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Review technical deliverables to ensure quality, consistency, and architectural compliance.
Stakeholder Communication
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Translate complex technical decisions into clear, structured communication for non-technical stakeholders.
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Participate in client discussions where architectural clarity or technical assurance is required.
Requirements
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.