Design and development of interactive and scalable web apps using Next.js and ReactJs, applying modern Front-End design principles. Utilization of Tailwind CSS for efficient and responsive styling. Integration of unit and end-to-end tests with Cypress to ensure code quality and reliability.Expertise in creating monorepo architectures for complex projects, including administrative applications, user portals, and marketing sites. Implementation of robust cloud infrastructures using Firebase, Google Cloud Platform, and Terraform.Implementation of CI/CD via GitHub Actions for automated and reliable deployments. Integration of third-party services such as Stripe for payments and Sentry for error tracking.Use of Docker for containerization, facilitating consistent development and deployment. Proactive problem-solving and performance optimization for an exceptional user experience.

Responsible for developing user-facing features, building reusable components with ReactJs and SASS. Design and implementation of interactive and responsive user interfaces, focusing on user experience and performance. Utilization of modern coding practices such as React hooks and functional components to create scalable and maintainable solutions. Optimization of rendering and efficient management of application state to ensure optimal performance, even with complex data sets. Integration of third-party libraries and APIs to enhance application functionality, while maintaining a clean and modular architecture.

Engineered and implemented an innovative Optical Character Recognition (OCR) Web Application specifically designed for the TIFINAGH script, leveraging advanced Machine Learning techniques. The core of the application was built upon the VGG16 deep learning model, known for its effectiveness in image recognition tasks.Key aspects of the project included:1. Data Preparation: Curated and preprocessed a comprehensive dataset of TIFINAGH characters to train the model effectively.2. Model Adaptation: Fine-tuned the VGG16 architecture to optimize its performance for TIFINAGH script recognition, balancing accuracy and computational efficiency.3. Web Integration: Developed a user-friendly web interface that allows users to upload images or use their device's camera to capture TIFINAGH text for real-time recognition.4. Post-processing: Implemented advanced algorithms for character segmentation and text reconstruction to improve the accuracy of the OCR results.5. Performance Optimization: Employed techniques such as transfer learning and model quantization to enhance the speed and efficiency of the recognition process.6. Cross-platform Compatibility: Ensured the web app functions seamlessly across various devices and browsers, making TIFINAGH OCR accessible to a wide user base.