Manager - Senior Manager | AI & Data

1. Role Summary

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)

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