Specialist - AI & Data Architect

Job description

Communication

Internal Communication:

Internal Business Units

All Internal Department

Senior Leadership Team

Purpose:

 Innovation in AI Products and Services

 Customer Experience Enhancement:

 Data Integration and Democratization

 Predictive and Prescriptive Analytics

 Change Management and Adoption of AI

 Cloud and Scalable AI Infrastructure

 Data Governance and Ethics

External Communicat...ion:

External IT Vendors

Software Integrators

Occupational Health & Safety and Environment

Accountability:

Are accountable for their acts and omissions.

Responsibility:

To follow agreed safe systems of work; to follow training and instructions; and to report accidents, incidents and near misses.

Authority:

To stop work if they think the work is unsafe.

Education & Professional Qualification:

Bachelor’s Degree in Computer Science, Data Science

Artificial Intelligence, Statistics, Mathematics, Engineering, Information Technology

Master’s Degree (preferred):

Data Science, Artificial Intelligence or Machine Learning, Computer Science, Business Analytics, Statistics or Applied Mathematics, Operations Research

Professional Experience:

• 8 years of hands-on experience in data science, machine learning, or AI roles, including developing and deploying AI models and algorithms.

• Experience with machine learning techniques, such as:

• Supervised/Unsupervised Learning

• Deep Learning (e.g., Neural Networks, CNNs, RNNs)

• Natural Language Processing (NLP)

• Computer Vision

Geographic Experience:

A plus

Computer Skills:

data science, machine learning, AI, including developing and deploying AI models and algorithms

Language Skills:

Business fluent English

Arabic Language is an advantage.

Strategic Leadership & Vision

• Define and execute the organization’s Data Science & AI strategy in alignment with business goals.

• Identify and prioritize opportunities where AI and data driven solutions enhance efficiency, customer experience, product offerings, and revenue generation.

• Lead the design of a unified data and AI architecture, including the development of a scalable Data Lakehouse, semantic layers, feature stores, and streaming/batch data integration.

• Drive the Generative AI roadmap—selecting LLM providers, designing RAG architectures, and enabling enterprise wide AI capabilities.

• Oversee FinOps practices to optimize cloud and compute costs associated with AI, GPU workloads, and large scale data storage.

AI & Data Engineering Execution

• Oversee the end to end development, validation, deployment, and lifecycle management of machine learning and AI models.

• Implement CI/CD/CT pipelines to ensure automated, continuous, and reliable model updates.

• Build and maintain vector database infrastructure to support semantic search, contextual AI, and long term knowledge retrieval.

• Ensure data pipelines, platforms, and architectures are optimized for performance, scalability, and resilience.

Governance, Risk, Compliance & Ethics

• Implement data governance, security, and privacy standards, including RBAC/ABAC and protection of PII and sensitive datasets.

• Establish and monitor automated controls for model drift, fairness, explainability, and ethical use of AI.

• Ensure compliance with relevant regulations (e.g., GDPR, HIPAA) and internal policies.

• Maintain best practices for data quality, versioning, reproducibility, and auditability.

Innovation & Continuous Improvement

• Lead experimentation with new algorithms, methods, and technologies to maintain a cutting edge AI ecosystem.

• Promote a culture of continuous improvement, refining processes, tools, and models to enhance performance and business impact.

Team Leadership & Capability Building

• Mentor and develop a high performing team of data scientists, AI engineers, and data professionals.

• Oversee training and upskilling initiatives to ensure the team remains current with emerging technologies and methodologies.

Stakeholder Management & Communication

• Serve as a strategic advisor between business and technical teams—translating business challenges into AI solutions and articulating AI outcomes clearly to non technical stakeholders.

• Provide regular updates to leadership on progress, performance metrics, model outcomes, and business results.

• Drive cross department collaboration to ensure successful adoption and integration of AI solutions.

Technology, Tools & Vendor Management

• Lead the selection, integration, and management of AI platforms, data tools, and vendor partnerships.

• Manage budgets for data science and AI initiatives, ensuring optimal allocation of resources.

• Make decisions related to hiring, vendor selection, and solutions procurement that support strategic AI objectives.

Education & Professional Qualification:

Bachelor’s Degree in Computer Science, Data Science

Artificial Intelligence, Statistics, Mathematics, Engineering, Information Technology

Master’s Degree (preferred):

Data Science, Artificial Intelligence or Machine Learning, Computer Science, Business Analytics, Statistics or Applied Mathematics, Operations Research

Professional Experience:

• 8 years of hands-on experience in data science, machine learning, or AI roles, including developing and deploying AI models and algorithms.

• Experience with machine learning techniques, such as:

• Supervised/Unsupervised Learning

• Deep Learning (e.g., Neural Networks, CNNs, RNNs)

• Natural Language Processing (NLP)

• Computer Vision

Geographic Experience:

A plus

Computer Skills:

data science, machine learning, AI, including developing and deploying AI models and algorithms

Language Skills:

Business fluent English

Arabic Language is an advantage.

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