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

Location
Dubai, Riyadh

Manager - Senior Manager – AI & Data

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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 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