Manager - Senior Manager | AI & Data | KSA - UAE

Role Summary

AI Solutioning and Product Delivery

• Design LLM applications such as copilots, document intelligence, and chatbots, as well as predictive and prescriptive analytics and recommendation or personalization engines.
• Translate business problems into deployable architecture covering data, models, integrations, APIs, security, scalability, and performance.
• Own client AI product roadmaps, use-case priorities, MVP iteration, success metrics, and adoption measures; guide build, buy, or partner decisions.
• Lead delivery from discovery and feasibility through data readiness, model development and validation, deployment, monitoring, and iteration.
• Establish model lifecycle, testing, quality assurance, operational monitoring, and retraining practices; ensure solutions are production-ready rather than proofs of concept.
• Enable client handover and long-term operability, guide change management, and raise delivery risks and dependencies early.

Client Leadership and Cloud Architecture

• Explain trade-offs, risks, and design decisions to client stakeholders in business terms; serve as the senior technical authority and review and sign off on designs and outputs.
• Mentor data scientists, ML engineers, and architects; mentoring is desirable but not mandatory.
• Architect solutions primarily on Azure, including Azure AI Services, Azure OpenAI, and Azure ML; use AWS SageMaker or Bedrock and GCP Vertex AI where relevant.
• Align solutions with enterprise architecture and security standards and account for applicable Middle East data-sovereignty constraints.

Experience and Expertise

• Requires 9–14+ years in data, analytics, or AI roles with direct delivery accountability.
• Requires strong understanding of machine learning fundamentals, applied AI, generative AI and LLM architectures, and data engineering concepts for AI workloads.
• Requires proven production-grade AI delivery experience, not only prototypes or proofs of concept.
• Requires practical understanding of AI governance, model risk, and responsible AI.
• Requires client-facing experience, ability to balance technical depth with commercial and delivery constraints, and strong communication with technical and non-technical audiences.
• Exposure to LLM orchestration, prompt engineering, RAG, and model evaluation, bias, and performance is listed as a capability area.
• Cloud and engineering experience or exposure to at least one hyperscaler is desirable; ability to design scalable, secure AI architectures is noted.