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.
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.
