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

Role summary

AI solution architecture

• Lead solution design for 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 ingestion and preparation, model selection and evaluation, application integration and APIs, security, scalability, and performance.

AI products and delivery

• Own client AI product roadmaps, use-case prioritization, MVP definition and iteration, success metrics, and adoption measures.
• Guide build-versus-buy-versus-partner decisions for models, platforms, and accelerators; design for production rather than proof-of-concept use.
• Lead discovery, feasibility, data readiness assessment, model development and validation, deployment, monitoring, and iteration.
• Establish practices for model lifecycle management, testing, quality assurance, operational monitoring, and retraining.
• Enable client technology-team handover and long-term operation, guide change management, and raise implementation risks and dependencies early.

Client and team leadership

• Explain trade-offs, risks, and design decisions to client stakeholders in business terms; serve as the senior technical authority on AI engagements.
• Review and sign off solution designs and delivery outputs; mentor data scientists, ML engineers, and architects as desirable, not mandatory.

Cloud and architecture

• Architect AI solutions primarily on Azure AI Services, Azure OpenAI, and Azure ML; use AWS SageMaker/Bedrock or GCP Vertex AI where client context requires.
• Align solutions with enterprise architecture and security standards and account for applicable Middle East data sovereignty constraints.

Experience and expertise

• 9–14+ years in data, analytics, or AI roles with direct delivery accountability.
• Strong understanding of machine learning fundamentals and applied AI, generative AI and LLM architectures, and data engineering for AI workloads.
• Proven delivery of production-grade AI solutions, beyond prototypes and proofs of concept.
• Practical understanding of AI governance, model risk, and responsible AI.

Additional capabilities

• Client-facing experience and ability to balance technical depth with commercial and delivery constraints.
• Strong communication for technical and non-technical audiences.
• Exposure to LLM orchestration, prompt engineering, retrieval-augmented generation, and model evaluation, bias, and performance considerations.
• Experience or exposure to at least one hyperscaler is desirable.