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

Manager - Senior Manager – AI & Data About Deloitte: When you work for us, you commit to a career at one of the largest and most prestigious professional services firms in the world. We have received numerous awards over the last few years, including Best Employer in the Middle East, and Best Consulting Firm, and the Middle East Training & Development Excellence Award. Our Purpose Deloitte makes an impact that matters. Every day we challenge ourselves to do what matters most—for clients, for our people, and for society. We serve clients distinctively, bringing innovative insights, solving complex challenges and unlocking sustainable growth. We inspire our talented professionals to deliver outstanding value to clients, providing an exceptional career experience and an inclusive and collaborative culture. We contribute to society, building confidence and trust in the markets, upholding the integrity of organizations and supporting our communities. Our shared values guide the way we behave to make a positive, enduring impact: - Lead the way - Serve with integrity - Take care of each other - Foster inclusion - Collaborate for measurable impact 1. Role Summary The Senior Manager – AI & Data will lead the design and delivery of complex AI and GenAI solutions for clients across public and private sectors. The role is techno‑functional, requiring strong hands‑on capability in solution architecture, AI product design, and end‑to‑end delivery of AI initiatives—from problem framing through production deployment and value realization. This role is accountable for solution quality, technical design decisions, and delivery outcomes , and it is not just limited to advisory and oversight. 2. Key Responsibilities AI / GenAI Solutioning & Architecture - Lead end‑to‑end solution design for AI and GenAI applications , including: o LLM‑based applications (e.g., copilots, document intelligence, chatbots) o Predictive and prescriptive analytics o Recommendation and personalization engines - Translate business problems into deployable AI solution architecture , covering: o Data ingestion and preparation o Model selection and evaluation o Application integration and APIs o Security, scalability, and performance considerations AI Product & Use Case Development · Define and own AI product roadmaps for client engagements: o Use case prioritization o MVP definition and iteration o Success metrics and adoption measures · Guide teams on build vs. buy vs. partner decisions for models, platforms, and accelerators · Ensure AI solutions are designed for real‑world production use , not PoCs End‑to‑End AI Delivery (with support of your delivery team): - Lead full AI project lifecycle: - Discovery and feasibility - Data readiness assessment - Model development and validation - Deployment, monitoring, and iteration - Establish practical delivery patterns for: - Model lifecycle management - Testing and quality assurance - Operational monitoring and retraining - Work closely with client technology teams to enable handover and long‑term operability. Guide teams for change management - Flag risks and dependencies on project delivery to raise concerns early during implementation Client & Team Leadership - Engage directly with client stakeholders to explain trade‑offs, risks, and design decisions in business terms - Act as the senior technical authority on AI engagements - Review and sign off on solution designs and delivery outputs - Mentor technical teams (data scientists, ML engineers, architects) (Desirable but not mandatory) Cloud & Platform Implementation - Architect and solution problem statements on cloud platforms , primarily: - Azure (Azure AI Services, Azure OpenAI, Azure ML) - AWS (Sagemaker, Bedrock) – where client context required - GCP (Vertex AI) – where relevant - This would require to think from an AI product development and deployment perspective rather than POCs for a/b tests - Ensure alignment with enterprise architecture and security standards - Apply Data Sovereignty constraints applicable in middle east while reviewing and proposing final solution architecture 3. Required Experience & Skills Technical & Functional Expertise - 9 –14+ years of experience in data, analytics, or AI roles with direct delivery accountability - Strong understanding of: - Machine learning fundamentals and applied AI - Generative AI and LLM architectures - Data engineering concepts supporting AI workloads - Proven experience delivering production‑grade AI solutions, not just prototypes and POCs AI & GenAI Capabilities - Exposure to: - LLM orchestration frameworks - Prompt engineering and retrieval‑augmented generation (RAG) - Model evaluation, bias, and performance considerations - Practical understanding of AI governance, model risk, and responsible AI considerations Consulting & Leadership - Experience working in client‑facing environments - Ability to balance technical depth with commercial and delivery constraints - Strong communication ski

9 –14+ years of experience in data, analytics, or AI roles with direct delivery accountability. Strong understanding of machine learning fundamentals and applied AI. Generative AI and LLM architectures. Data engineering concepts supporting AI workloads. Proven experience delivering production-grade AI solutions, not just prototypes and POCs. Exposure to LLM orchestration frameworks, prompt engineering and retrieval-augmented generation (RAG), model evaluation, bias, and performance considerations. Practical understanding of AI governance, model risk, and responsible AI considerations. Experience working in client-facing environments. Ability to balance technical depth with commercial and delivery constraints. Strong communication skills.

Lead end-to-end solution design for AI and GenAI applications, including LLM-based applications (copilots, document intelligence, chatbots), predictive and prescriptive analytics, and recommendation/personalization engines. Translate business problems into deployable AI solution architecture covering data ingestion, model selection, application integration, security, scalability, and performance. Define and own AI product roadmaps for client engagements, including use case prioritization, MVP definition, success metrics, and adoption measures. Guide build vs. buy vs. partner decisions for models, platforms, and accelerators. Ensure AI solutions are designed for production use. Lead full AI project lifecycle from discovery to deployment, data readiness, model development, deployment, monitoring, and retraining. Establish delivery patterns for model lifecycle management and QA. Work with client technology teams for handover and long-term operability, flag risks early. Engage with client stakeholders to explain trade-offs, risks, and design decisions. Serve as the senior technical authority on AI engagements and mentor technical teams.