Developed a Python-based automation system utilising the Azure Bing Web Search API to scrape and aggregate entry-level software engineering roles from various job boards. Implemented a backend notification service using Simple Mail Transfer Protocol (SMTP) to deliver curated, deduplicated job listings via automated daily email updates. Created advanced filtering logic to prioritise specific Applicant Tracking Systems (ATS) like Greenhouse and Workday, ensuring higher quality leads for candidate applications. Configured automated workflows using GitHub Actions and Cron jobs to ensure reliable, consistent execution of data retrieval and distribution processes. Applied Object-Oriented Programming (OOP) principles to design a modular codebase, facilitating the integration of additional search parameters and technical role categories. Participated in testing and debugging the search algorithms to eliminate redundant data and improve the overall precision of the automated alerts.

Developed a deep learning system for medical image classification using Python and Depthwise Separable Convolutional Neural Networks (DS-CNN). Built a full-stack web application featuring a React.js frontend and a Flask/FastAPI backend to provide real-time diagnostic predictions via Representational State Transfer (REST) APIs. Implemented Object-Oriented Programming (OOP) principles to structure the machine learning pipeline, ensuring modularity and scalability of the codebase. Created automated data preprocessing scripts to normalise and augment Magnetic Resonance Imaging (MRI) datasets, improving model robustness and precision. Tested system reliability by performing rigorous evaluation of model accuracy using statistical metrics and validation datasets to ensure high-performance results. Collaborated on detailed technical documentation for the model architecture and deployed the application via Netlify for cross-platform accessibility.

Developed a high-fidelity web application clone using React.js to demonstrate proficiency in component-based architecture and modern frontend development. Created responsive user interfaces by implementing CSS3 and HTML5, ensuring a seamless user experience across various device screen sizes. Implemented state management and hooks within React.js to handle dynamic data rendering and improve application interactivity. Designed the application following Model-View-Controller (MVC) principles to maintain a clean separation of concerns and modular code structure. Tested the application functionality and debugged User Interface (UI) components to ensure cross-browser compatibility and optimal performance. Collaborated on version control using Git to manage source code updates and maintain a structured development workflow.

Built an Arduino-based drowsiness detection system utilising Optical Blink Sensors to monitor real-time eye movement for safety applications. Implemented detection algorithms using C++ and Java-influenced logic to interpret sensor data and trigger timely alerts for sleepiness. Integrated Internet of Things (IoT) components with microcontroller hardware to ensure low-latency communication and responsive system behaviour. Debugged and optimised sensor sensitivity thresholds to improve detection accuracy across various lighting environments and user conditions. Supported the full prototype development cycle by testing hardware-software interactions and maintaining detailed technical documentation. Applied Object-Oriented Programming (OOP) principles to organise the codebase, ensuring modularity and easier integration of additional sensor modules.