Building the frontend for CAID, an AI-powered carbon emissions dashboard, focusing on data visualization, usability, and seamless integration with AI services.

• Developing the frontend for CAID, an AI-driven carbon emissions analytics dashboard, using TypeScript, React, and Next.js to help sustainability teams visualize and track their impact.
• Integrating backend services and AI models with the frontend via REST APIs, enabling near real-time carbon insights and reducing manual data handling for users.
• Collaborating with designers and ML engineers to ship responsive, accessible UI components, and containerizing the app with Docker and Kubernetes for scalable, reliable deployments.

Worked on grid software and smart infrastructure initiatives focusing on forecasting, dashboards, and IoT integrations.

• Trained and deployed LSTM based AI models on smart meter data for consumption forecasting.
• Built Power BI dashboards with live MDMS data for Kahramaa LV visibility.
• Configured Modbus, BACnet, MQTT to stream MFM and solar metrics to IoT gateways.
• Supported technical sales docs for PSS SINCAL proposal for 2.8M QAR EWA bid.

Smart Warning System for Distracted Delivery Motorcyclists Using Machine Learning

• Contributed to the Development of a CNN-based mobile application that can detect rider distraction even when motorcyclists are wearing helmets
• Created a custom data set, comprised of 1,799 images (balanced: “Looking” vs. “Not Looking”), in different lighting conditions, helmets and backgrounds.
• Built a Flutter-based mobile app that runs passively in the background and delivers over the screen alerts, which reduced distractions without sudden interruptions.