I work across AI engineering, software quality, and data-driven program evaluation. My background spans mechatronics, data analysis, and applied machine learning, and I am currently completing a Master’s in Artificial Intelligence at the Arab American University, where I maintain a strong academic record and contribute to research and real-world AI projects. My experience includes building end-to-end data and AI solutions, such as CerebroX — a full EDA automation engine that I later expanded into an interactive GUI application for data cleaning, validation, and structured reporting. I’ve also developed rule-based NLP systems, including an Arabizi-to-Arabic conversion engine constructed entirely through linguistic rules rather than machine learning. In the health sector, I presented research at the IEEE MIUCC conference on a machine-learning framework for the early prediction of kidney dysfunction in diabetic patients. My work focuses on transparent, interpretable models that support real-world decision-making. Alongside AI development, I serve as a QA Engineer at CodeFusion, where I apply structured testing methods for web, mobile, and API systems, combining manual and automated practices. I also work as a MEAL Coordinator at Al Nayzak, supporting program monitoring, data systems, and accountability processes across development projects. Across all roles, I aim to build systems that are reliable, interpretable, and practically useful. I’m especially motivated by opportunities that combine AI engineering, data analytics, and problem-solving to create solutions with measurable impact.…I work across AI engineering, software quality, and data-driven program evaluation. My background spans mechatronics, data analysis, and applied machine learning, and I am currently completing a Master’s in Artificial Intelligence at the Arab American University, where I maintain a strong academic record and contribute to research and real-world AI projects. My experience includes building end-to-end data and AI solutions, such as CerebroX — a full EDA automation engine that I later expanded into an interactive GUI application for data cleaning, validation, and structured reporting. I’ve also developed rule-based NLP systems, including an Arabizi-to-Arabic conversion engine constructed entirely through linguistic rules rather than machine learning. In the health sector, I presented research at the IEEE MIUCC conference on a machine-learning framework for the early prediction of kidney dysfunction in diabetic patients. My work focuses on transparent, interpretable models that support real-world decision-making. Alongside AI development, I serve as a QA Engineer at CodeFusion, where I apply structured testing methods for web, mobile, and API systems, combining manual and automated practices. I also work as a MEAL Coordinator at Al Nayzak, supporting program monitoring, data systems, and accountability processes across development projects. Across all roles, I aim to build systems that are reliable, interpretable, and practically useful. I’m especially motivated by opportunities that combine AI engineering, data analytics, and problem-solving to create solutions with measurable impact.WWWW…