Role Overview We are looking for a Senior AI Architect to lead the enterprise-wide design, governance, and deployment of AI platforms and services across Microsoft Azure and multi-cloud environments. This role sits at the intersection of AI innovation, cloud architecture, security, and governance, ensuring AI is deployed as a trusted, scalable, compliant, and enterprise-grade capability. You will define the end-to-end AI architecture while advising leadership on risk, compliance, and strategic AI adoption. Key Responsibilities???? Own and design enterprise AI architecture across Azure, Google Cloud, and hybrid environments ???? Define AI platform standards including MLOps, model deployment, APIs, monitoring, and observability ???? Establish and enforce AI governance, compliance, and risk frameworks (privacy, bias, explainability, regulatory alignment) ???? Architect secure and scalable AI platforms using Azure ML, Azure AI Services, Synapse, Data Factory, and Data Lake ???? Define and govern model lifecycle management (approval, versioning, retraining, auditability, decommissioning) ???? Enable multi-cloud interoperability across Azure, GCP, and AWS ???? Design AI security architecture (IAM, encryption, data isolation, zero-trust principles) ???? Lead integration of AI solutions with ERP (SAP), CRM, data platforms, and enterprise APIs ???? Provide architectural oversight and governance across all AI initiatives ???? Advise senior leadership and stakeholders on AI strategy, compliance, and enterprise risk ???? Mentor AI engineers, data scientists, and IT teams on enterprise AI best practices Qualifications???? Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (required) ???? Master’s degree in AI, Data Science, Cloud Computing, or Enterprise Architecture (preferred) Certifications (Highly Valued)✔ Microsoft Azure Solutions Architect Expert or Azure AI Engineer ✔ Cloud & Architecture certifications (AWS, Google Cloud, TOGAF, etc.) Technical Expertise⚙️ Microsoft Azure AI & Data platforms (Azure ML, Synapse, Cognitive Services, Data Factory, Data Lake) ⚙️ AI platform architecture: MLOps, model registries, API management, monitoring ⚙️ Multi-cloud and hybrid AI architectures ⚙️ AI & data governance, privacy, security, and regulatory compliance ⚙️ AI risk management: bias mitigation, explainability, human-in-the-loop, auditability ⚙️ Enterprise system integration and secure data exchange Language Requirements????️ English: Fluent (required) ????️ Arabic: Highly desirable