Data and AI Specialist

Job Description Deep technical expertise in data engineering, machine learning, and cloud AI services with strong governance and policy awareness, ensuring scalable, secure, and ethical use of AI in alignment with organizational goals. Responsibilities • Design, build, and manage data pipelines and analytics platforms using Azure Data Factory, Synapse Analytics, and Data Lake. • Develop, train, and deploy machine learning and AI models on Azure Machine Learning and integrate with other cloud platforms where needed. • Implement data governance, security, and compliance controls across multi-cloud environments. • Contribute to and support the organization’s AI and data strategy, ensuring alignment with digital transformation initiatives. • Ensure multi-cloud interoperability, including the ability to migrate and optimize AI workloads across Azure, Google Cloud, and AWS. • Conduct data analysis, reporting, and visualization using Power BI, Tableau, or equivalent tools. • Evaluate and adopt emerging AI/ML services from multiple cloud providers to maximize value. • Document and enforce AI governance frameworks, policies, and ethical guidelines. • Collaborate with IT, governance bodies, and business stakeholders to identify AI use cases that deliver tangible business outcomes. • Provide technical mentorship, training, and awareness sessions on AI and data tools to internal teams. Qualifications • Bachelor’s degree in computer science, Data Science, AI, or related field (required). • Master’s degree in AI, Business Analytics, or Information Systems (preferred). Certifications • Azure certifications (Azure AI Engineer Associate, Azure Data Scientist, or Azure Solutions Architect) are highly desirable. • Multi-cloud or vendor certifications (e.g. Google Cloud Professional ML Engineer, etc.) are a plus. Technical Expertise • Strong hands-on experience with Microsoft Azure AI and Data Services (Azure ML, Synapse, Cognitive Services, Data Factory, Data Lake). • Familiarity with multi-cloud integration and migration strategies. • Proficiency in Python, R, SQL, and AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn). • Knowledge of data governance, MLOps, and ethical AI frameworks. • Experience in data visualization with Power BI, Tableau, or equivalent. • Strong problem-solving and system integration skills. • Fluency in English (required); proficiency in Arabic is highly desirable. Personal Competencies • Strategic mindset with the ability to bridge AI innovation and business needs. • Strong communication skills to engage both technical and non-technical audiences. • Agile, adaptable, and experienced in multi-cloud and hybrid environments. • High integrity in managing data, privacy, and ethical considerations. • Collaborative and proactive in cross-functional teams. Show more Show less

Bachelor’s degree in computer science, Data Science, AI, or related field (required). Master’s degree in AI, Business Analytics, or Information Systems (preferred). Azure certifications (Azure AI Engineer Associate, Azure Data Scientist, or Azure Solutions Architect) are highly desirable. Multi-cloud or vendor certifications (e.g. Google Cloud Professional ML Engineer, etc.) are a plus.

Design, build, and manage data pipelines and analytics platforms using Azure Data Factory, Synapse Analytics, and Data Lake. Develop, train, and deploy machine learning and AI models on Azure Machine Learning and integrate with other cloud platforms where needed. Implement data governance, security, and compliance controls across multi-cloud environments. Contribute to and support the organization’s AI and data strategy, ensuring alignment with digital transformation initiatives. Ensure multi-cloud interoperability, including the ability to migrate and optimize AI workloads across Azure, Google Cloud, and AWS. Conduct data analysis, reporting, and visualization using Power BI, Tableau, or equivalent tools. Evaluate and adopt emerging AI/ML services from multiple cloud providers to maximize value. Document and enforce AI governance frameworks, policies, and ethical guidelines. Collaborate with IT, governance bodies, and business stakeholders to identify AI use cases that deliver tangible business outcomes. Provide technical mentorship, training, and awareness sessions on AI and data tools to internal teams.