I'm a passionate Software Engineer with expertise in machine learning and AI, currently focused on developing innovative solutions at the intersection of artificial intelligence and user-facing applications. I bring a unique combination of skills across software development and machine learning, and I love building scalable, efficient ML systems that drive real-world impact.
I'm focused on advancing my career in software engineering, with a particular interest in AI-driven solutions and server-side innovation. I'm deeply committed to continuous learning and staying at the forefront of emerging technologies. Always excited to connect with like-minded technologists who are passionate about building cutting-edge products that solve meaningful problems at scale.
- Languages: JavaScript, Python, PHP, Java
- Frameworks & Libraries: React, Next.js, Node.js, FastAPI, Spring Boot
- Tools & Platforms: AWS, Docker, Netlify
- ML/DL & Data Science: Keras, PyTorch, Scikit-learn, Pandas, Numpy, OpenCV
- RoundRobinPro: Developed a program simulating Round Robin CPU scheduling, analyzing processes via performance metrics such as response time and turnaround time for different time quantum values.
- VisNet: Applied transfer learning to pre-trained CNN models, leading to the selection of GoogLeNet, which outperformed VGGNet16 and ResNet with a 78.7% accuracy on the CIFAR-10 dataset.
- StockPulse: Developed Single Page Application (SPA), integrating the Alpha Vantage API for real-time stock data retrieval, ensuring accuracy with data from the last 10 trading days.
- HousingPricePredictor: AWS deployed Spark-based machine learning project, training and evaluating multiple regression models on California's housing data, with Random Forest Regression proving most effective in predicting house prices.