Building scalable cloud-native applications and intelligent systems
- Full-Stack Developer and AI/ML Engineer with a Master’s in Computer Science
- Passionate about building scalable systems, AI-driven applications, and cloud-native solutions
- Open to Software Engineering, AI/ML, and Full-Stack roles
- Currently focused on building production-level projects with real-world impact
🔹 Breast Cancer Detection
CNN-based medical imaging pipeline using image enhancement, segmentation (Watershed & Canny), and deep learning classification achieving up to 99.67% accuracy.
🔹 Shrimp Disease Prediction
Deep learning model using EfficientNet-B0 for aquaculture disease detection, achieving high precision in identifying infected samples.
🔹 AI-Enabled B2B Invoice Management
Full-stack application built with React + Flask, integrating machine learning models for invoice prediction, anomaly detection, and automated workflows.
🔹 BloodBridge — Donor & Patient Platform
Real-time system for connecting blood donors and patients, featuring dynamic data handling, user management, and responsive UI for emergency coordination.
🔹 Fitness Tracker Application
User-centric fitness tracking system with analytics, activity monitoring, and performance insights using full-stack architecture.
🔹 CI/CD Pipeline (GCP + Kubernetes)
Designed an automated deployment pipeline using Google Cloud Platform, Docker, and Kubernetes, reducing manual deployment effort and improving reliability.
🔹 Containerized Web Application Built and deployed scalable web services using Docker and Kubernetes, enabling efficient resource utilization and cloud-based deployment.
