Kuwait City, Al Asimah, Kuwait
I help organizations turn AI opportunities into real business outcomes. I work closely with customers to understand their goals, design tailored AI solutions, and demonstrate how these technologies can deliver measurable value. I enjoy collaborating with stakeholders, uncovering use cases, and guiding teams through every stage, from ideation to solution design. My approach combines technical expertise with a consultative mindset, ensuring that every engagement drives impact and builds long-term partnerships. Always happy to connect and exchange ideas on how AI can transform the way we work.
AI Engineer Intern at FADEL
Software Engineering Fellow at Headstarter AI
AI Engineer Intern at CODE Technologies partners with Microsoft, Google, Citrix and CloudBlue.
• Engineered an educational game designed to assess and enhance computational thinking (CT) abilities in K-12 students, aiming to introduce CT as a core subject in collaboration with Springfield schools. • Supported the design and implementation of computational thinking assessments through rigorous classroom observations, family interviews, and teacher feedback, aiming to validate the effectiveness of instructional materials • Contributing to a larger NSF-funded project, recognized with a CSForAll award, focusing on innovation and inclusion in educational technology for young learners, with collaboration from researchers at the University of Michigan Ann Arbor, the University of Massachusetts Amherst, and other institutions.
• Serve as a representative of the College of Information and Computer Sciences (CICS), delivering informative presentations to prospective students and their families, both in-person and virtually, to provide insights into the CICS program and student life at UMass. • Collaborate closely with academic advisors during key events, such as Fall Visits and Destination Day, ensuring a smooth and engaging experience for visitors. • Enhance public speaking skills through regular presentations, effectively communicating complex information in a clear and relatable manner.
• Fine-tuned LLaMA-2-7B model for system initiative prediction using the MSDialog dataset, QLoRA technique, and Hugging Face libraries; achieved 76.7% accuracy with fine-tuning and 88% with few-shot learning. • Collaborated with a team of undergraduates, under PhD mentorship, on a classification task, leveraging Google Colab and pandas for data preprocessing and experimentation. • Presented research findings to the program members and coordinated team communication via Slack.