Find The RightJob.

Specialist - AI & Data Architect

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 Communication:
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.

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

Similar jobs