Senior Manager AI & Data

About the role

The Senior Manager AI & Data leads the design and delivery of complex AI and GenAI solutions for clients across public and private sectors. It is a techno-functional role requiring strong hands-on capability in solution architecture, AI product design and end-to-end delivery, from problem framing through production deployment and value realization. The role is accountable for solution quality, technical design decisions and delivery outcomes.

Key responsibilities

• Lead end-to-end solution design for AI and GenAI applications, including LLM-based apps such as copilots, document intelligence and chatbots, predictive and prescriptive analytics, and recommendation engines
• Translate business problems into deployable AI architecture covering data ingestion, model selection and evaluation, API integration, security, scalability and performance
• Own AI product roadmaps: use-case prioritization, MVP definition, success metrics, and guide build vs. buy vs. partner decisions
• Lead the full AI project lifecycle with the delivery team: discovery, data readiness, model development and validation, deployment, monitoring and iteration, and operational retraining
• Work with client technology teams on handover and long-term operability, flag risks early and guide change management
• Engage directly with client stakeholders, act as senior technical authority, review and sign off designs, and mentor data scientists, ML engineers and architects
• Architect cloud solutions on Azure (Azure AI Services, Azure OpenAI, Azure ML), AWS (SageMaker, Bedrock) and GCP (Vertex AI) where needed, applying data sovereignty constraints in the Middle East

Required profile

• 9 to 14+ years of experience in data, analytics or AI roles with direct delivery accountability
• Proven experience delivering production-grade AI solutions, not just prototypes and POCs
• Client-facing consulting experience and ability to balance technical depth with commercial and delivery constraints
• Strong communication skills for technical and non-technical audiences

Required skills

• Machine learning fundamentals, generative AI and LLM architectures, and data engineering concepts for AI workloads
• LLM orchestration, prompt engineering and RAG, model evaluation, bias and performance considerations, and responsible AI
• Exposure to at least one hyperscaler (Azure, AWS, GCP) and ability to design scalable, secure AI architectures