Key Responsibilities:AI Strategy & Consultancy: Act as a senior consultant to identify and assess AI use cases, evaluating them for business value, technical feasibility, and architectural fit within the digital transformation roadmap. Architecture Design: Architect and oversee the deployment of end-to-end AI stacks using Azure AI Foundry and on-premise enterprise solutions. This includes designing for Inference Engines, Vector Databases, and Agent Builders. Sizing & Cost Estimation: Perform detailed technical sizing of AI infrastructure (Compute/GPU/Token usage) and provide accurate cost-benefit analysis for both Azure cloud and on-premise implementations. Agentic & Gen-AI Leadership: Lead the design and implementation of Agentic-AI workflows and Multi-Agent Systems, ensuring robust orchestration and goal-alignment. LLM Finetuning for Arabic language is also needed. Operations (MLOps/LLMOps/Agent Ops): Establish and enforce standards for the AI lifecycle, focusing on automated deployment, monitoring of agentic behavior, and model performance optimization. Hybrid Integration: Design architectures that bridge on-premise data security with Azure AI capabilities, ensuring seamless data flow between the AI stack and the Medallion data architecture. Compliance & Governance: Ensure all AI architectures comply with federal AI ethics guidelines, focusing on data privacy and responsible AI. Technical Troubleshooting: Resolve complex issues related to model latency, vector embedding quality, and integration bottlenecks within the AI pipeline. Leadership & Mentoring: Guide AI engineers and data scientists, fostering a culture of excellence in all data activities. Qualifications:Certifications:Microsoft Azure AI Engineer Associate and Azure Solutions Architect Expert certification. Experience: Minimum of 8 years in AI/Machine Learning, with at least 3 years in a Lead Architect capacity for large-scale enterprise projects. Hands-on Mastery: Deep experience with Azure AI Foundry, Vector Databases (e.g., Weaviate, Azure AI Search, v LLM), and Inference Engines (e.g., Nvidia Triton, v LLM, etc) Advanced AI Paradigms: Proven track record in delivering Agentic-AI and Generative AI solutions at an enterprise level. Technical Stack: Proficiency in AI and ML frameworks, Python, Agentic Workflow, and containerization (Kubernetes/Docker). Consultancy Skills: Strong ability to translate complex AI concepts into architectural blueprints and financial estimates for stakeholders. Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field. Language: Proficiency in Arabic and English is required Competencies:AI Technical Leadership: Ability to set the vision for the AI stack. Financial Acumen: Proficiency in AI cost modeling (Tokenomics & Infrastructure). Analytical Thinking: Breaking down business problems into AI-solvable components. Collaboration: Working closely with the Data Architect Lead to leverage enterprise data for AI. Adaptability: Staying ahead of the rapidly evolving AI landscape