Aspiring Software Developer specializing in Artificial Intelligence and Machine Learning. Final year B.Tech IT student with hands-on experience in developing ML models, data analysis pipelines, and full-stack applications.
Passionate problem-solver with a drive to leverage AI/ML for real-world impact
I'm a final year B.Tech Information Technology student at Perunthalaivar Kamarajar Institute of Engineering and Technology with a strong foundation in software development, artificial intelligence, and machine learning.
My expertise spans across developing sophisticated machine learning models using frameworks like TensorFlow and Scikit-learn, building data analysis pipelines with Pandas and visualization tools, and creating full-stack applications with modern technologies including FastAPI, React Native, and cloud platforms like IBM Cloud and AWS.
I'm a strong problem-solver and quick learner, ready to apply theoretical knowledge to tackle practical engineering challenges. I thrive in collaborative environments and am constantly seeking opportunities to expand my skill set and contribute to innovative projects.
Hands-on learning through industry internships and collaborations
Performed analytical operations and data preprocessing to improve process clarity through comprehensive documentation. Applied machine learning principles on actual datasets to derive meaningful insights.
Executed ML model construction, data analysis, data cleaning and model testing activities. Gained valuable experience with industry-level tools and processes.
Contributed to ML model construction and data analysis activities. Developed expertise in IBM Cloud tools and machine learning workflows.
Developed FastAPI frameworks and AWS Lambda functions. Created simplified data processing pipelines using structured data rules, boosting model training efficiency by 30%. Contributed to project implementation and software development tasks.
Strengthened core Java programming concepts through hands-on practice and mentorship.
Innovative solutions leveraging AI/ML and modern development practices
Advanced deepfake detection using ResNeXt-101 + LSTM/Attention architecture with MTCNN preprocessing. Features REST API, JSON reporting, adversarial robustness testing, and local GPU deployment pipeline.
RandomForestClassifier NIDS on KDD Cup 1999/NSL-KDD with 41 features, Watson ML deployment, AutoAI time-series pipeline, and REST API testing for scalable cloud anomaly detection.
Random Forest fire classification on NASA MODIS data (2021–2023) with SMOTE balancing, thermal/geospatial features, and complete ML pipeline from EDA to production deployment.
Production RAG system for scalable job description query responding using document ingestion, ChromaDB vector storage, FastAPI endpoint, Hugging Face LLM, and pytest testing.
Comprehensive EDA on Netflix content trends and Airbnb pricing patterns using Pandas/Seaborn visualizations analyzing ratings, genres, and review-price correlations.
Mobile application for calculating and tracking student CGPA with optimized storage solutions achieving 30% reduction in data access time for production reports.
Feel free to reach out for collaborations or opportunities