Sakshi Jadhav

Detail-oriented Computer Engineering graduate with 11 months of hands-on experience as a Full Stack Developer Intern. Proven track record of delivering end-to-end web applications using the MERN stack and integrating AI functionalities to enhance user experience. Combines strong software engineering principles with practical machine learning expertise, highlighted by a deep learning-based final-year project for cattle breed detection. Eager to bring a blend of robust full-stack development and predictive AI capabilities to a dynamic engineering team.

• Certificate Course in C++ Programming - Disha Computer Institute
• Certificate Course in DSA - Disha Computer Institute
• Certificate Course in Database Management System - Disha Computer Institute

Visitor Management System | MERN Stack, JWT, Tailwind CSS

• Designed and deployed a secure, full-stack visitor tracking application to streamline check-ins, badge generation, and host notifications.
• Implemented role-based authentication using JWT to separate administrative controls from front-desk and visitor interfaces.
• Optimized database schemas in MongoDB to efficiently log, search, and archive historical visitor data for compliance reporting.

ERP Software Suite | MERN Stack, Redux Toolkit, REST APIs

• Contributed to building a scalable Enterprise Resource Planning (ERP) suite featuring modules for inventory tracking, employee management, and financial reporting.
• Utilized Redux for robust state management across complex, multi-page data tables and real-time dashboards.
• Integrated third-party APIs and built custom middleware to handle automated reporting and data exports (PDF/CSV).

Cab Booking System | MERN Stack, Socket.io, Google Maps API

• Developed a dynamic ride-hailing web application featuring real-time location tracking and driver-rider matchmaking.
• Integrated the Google Maps API for interactive route rendering, distance calculations, and address autocomplete.
• Leveraged Socket.io to establish low-latency, bi-directional communication for live ride status updates and driver location polling.

Cattle Breed Detection System | Python, PyTorch/TensorFlow, OpenCV, CNNs

• Engineered a computer vision application for the agricultural sector to automate the identification of cattle breeds from image and video feeds.
• Preprocessed and augmented a custom dataset using OpenCV, enhancing model robustness against varying lighting conditions and angles.
• Trained and compared multiple deep learning architectures (e.g., Custom CNNs, ResNet, MobileNet), achieving high classification accuracy through hyperparameter tuning and transfer learning.