Artificial Intelligence Engineer (UAE National)

AI Strategy and Vision - Develop a comprehensive AI and data science strategy that aligns with the company’s objectives and long-term vision. - Identify and evaluate opportunities for integrating AI and data science into existing processes and operations to improve efficiency and decision making. - Lead end-to-end project management of AI initiatives, from conception to deployment and post-implementation evaluation. Collaborate with cross-functional teams to define project scope, requirements, and deliverables. Data Analysis and Modeling - Oversee the development of machine learning models and algorithms for various applications, such as predictive analytics, natural language processing, and computer vision. - Ensure data quality and integrity and implement best practices for data preprocessing and feature engineering and model evaluation. - Evaluate and select appropriate tools, technologies and frameworks for data analysis and modeling. - Establish monitoring mechanisms to track project performance, identify areas for improvement, and report progress to stakeholders. - Create repeatable, interpretable, dynamic, and scalable models seamlessly incorporated into analytic data products Innovation and Research - Stay abreast of the latest developments and trends in AI and data science, exploring innovative solutions and emerging technologies that can benefit the organization. - Regularly evaluate the performance of AI projects and data science initiatives and provide detailed reports to senior management. - Use data-driven insights to propose improvements and optimizations to existing processes Data Privacy - Ensure compliance with data privacy regulations and ethical guidelines throughout all data science activities. - Implement data protection measures and maintain confidentiality of sensitive information. - Identify potential risks and challenges associated with AI projects and data science initiatives

AI Strategy and Vision: Develop AI and data science strategy aligned with objectives; identify opportunities to integrate AI into processes; lead end-to-end AI initiatives from conception to deployment and post-implementation evaluation. Data Analysis and Modeling: Oversee development of ML models for predictive analytics, NLP, and computer vision; ensure data quality and preprocessing; evaluate tools and frameworks; establish monitoring and report progress to stakeholders; create scalable models integrated into analytic data products. Innovation and Research: Stay updated on AI trends; evaluate performance and provide reports to senior management; propose improvements using data-driven insights. Data Privacy: Ensure data privacy compliance and ethical guidelines; implement data protection; identify risks and challenges associated with AI projects.