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

Overview

About Deloitte

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
• 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.
• Key Responsibilities

AI / GenAI Solutioning & Architecture

• Lead end‑to‑end solution design for AI and GenAI applications, including:
• LLM‑based applications (e.g., copilots, document intelligence, chatbots)
• Predictive and prescriptive analytics
• Recommendation and personalization engines
• Translate business problems into deployable AI solution architecture, covering:
• Data ingestion and preparation
• Model selection and evaluation
• Application integration and APIs
• Security, scalability, and performance considerations

AI Product & Use Case Development

• Define and own AI product roadmaps for client engagements:
• Use case prioritization
• MVP definition and iteration
• 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

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 skills for technical and non‑technical audiences

Cloud & Engineering (desirable)

• Experience and exposure to at least one Hyperscaler
• Ability to design scalable, secure AI architectures