I'm a dedicated Software Engineer with experience designing and building robust, scalable web applications and backend systems. My expertise lies in leveraging modern technologies like Python, Flask, Node.js, Java, and Spring Boot to create efficient solutions.
I focus on writing clean, maintainable code, ensuring high performance, and delivering solutions that meet real-world needs.
University at Buffalo, The State University of New York
August 2023 - January 2025
Building scalable web applications and microservices
Worked with including CVS Health and Rubixe
Completed across diverse domains and technologies
Designed RESTful APIs with Spring Boot, reducing latency by 20%, and developed responsive React.js UIs that improved engagement by 15%. Deployed scalable services on AWS using Docker and Kubernetes, with CI/CD pipelines via GitHub Actions to cut release cycles by 30%. Proficient in Agile, JIRA, and system monitoring with ElasticSearch and Grafana.
Developed full-stack applications using the MERN stack, improving performance by 25%. Built responsive UIs with React.js and Redux, and implemented secure APIs with Node.js and Express.js. Integrated MongoDB for efficient data handling and collaborated with cross-functional teams to deliver high-quality solutions on time.
Built real-time financial dashboards with React and Redux, enabling dynamic data visualization. Developed Spring Boot microservices for payment workflows with high availability and integrated secure APIs using Spring Security and JWT.
An intelligent, document-aware chatbot leveraging Retrieval-Augmented Generation (RAG) for accurate, context-based medical assistance.
This project implements a document-aware chatbot using RAG to deliver medically accurate responses. Built using Flask, FAISS, and SentenceTransformers.
A BCNF-normalized PostgreSQL database for Olympic data with REST APIs and Power BI dashboards.
Developed a normalized sports DB system with REST API for analytics and interactive Power BI visualizations.
Combined CNN saliency maps with HOG descriptors to improve badge recognition accuracy using MLP.
Implemented CNN saliency + HOG features to enhance recognition tasks using a fully connected MLP network with visualization through Grad-CAM.
Looking for a skilled developer or have a project in mind? Feel free to reach out!
