Junior Software Engineer with a strong academic background in Object-Oriented Programming and modular systems. I’ve built several full-stack projects, from React + Spring Boot applications to cloud-based, serverless systems paired with React. The first project I've built was a full-stack web application (GameStoreApp) using React, a RESTful API with Spring Boot, and MySQL, strengthening my understanding of end-to-end web development and API-driven architecture. I developed this project as part of a university course that I deliberately chose because I wanted to start learning the frameworks and technologies I knew would be essential for my professional path. The core idea behind GameStoreApp was to build a simple but realistic Single Page Application (SPA) where a seller can upload and manage games, and users can browse, filter, and interact with them as clients. I've also built an AI-powered product classification & sentiment analysis project, where I trained and evaluated multiple ML/NLP models, selected the best-performing approach, and deployed it as a Django REST API. This was a personal project that I built to understand the full machine learning lifecycle beyond theory: from data preprocessing and feature engineering, to model training, evaluation, selection, and production deployment. The goal was not to use large language models, but to master the foundational, classical ML approach that underpins many real-world AI systems. Additionally, I explored a task-creation workflow that evaluates and tests LLM-based agents across domains; this was new to me, and by experimenting with it, I learned the basics of how complete evaluation tasks are structured and validated end-to-end. During my internship, I worked on the design and implementation of an end-to-end serverless logging pipeline for a production .NET web application, gaining hands-on experience with cloud-native architectures and real-world operational constraints. The 1st part of the project involved migrating application and error logs from an existing MS SQL Server–based logging approach to a fully serverless architecture. I designed and implemented a custom solution using AWS Lambda and DynamoDB, selected for scalability, simplicity, and cost efficiency. The backend .NET application was updated to send HTTP requests to this endpoint. I designed a unified log schema that combined application logs and error logs into a single DynamoDB table, ensuring consistent field structure, JSON serialization, and reliable ingestion through Lambda. The 2nd part of the project focused on log visualization and monitoring. I developed an independent React-based web application that consumes the logging API and displays logs in a structured dashboard. I also implemented dropdown filters, full-text search, and efficient log retrieval for large datasets.…Junior Software Engineer with a strong academic background in Object-Oriented Programming and modular systems. I’ve built several full-stack projects, from React + Spring Boot applications to cloud-based, serverless systems paired with React. The first project I've built was a full-stack web application (GameStoreApp) using React, a RESTful API with Spring Boot, and MySQL, strengthening my understanding of end-to-end web development and API-driven architecture. I developed this project as part of a university course that I deliberately chose because I wanted to start learning the frameworks and technologies I knew would be essential for my professional path. The core idea behind GameStoreApp was to build a simple but realistic Single Page Application (SPA) where a seller can upload and manage games, and users can browse, filter, and interact with them as clients. I've also built an AI-powered product classification & sentiment analysis project, where I trained and evaluated multiple ML/NLP models, selected the best-performing approach, and deployed it as a Django REST API. This was a personal project that I built to understand the full machine learning lifecycle beyond theory: from data preprocessing and feature engineering, to model training, evaluation, selection, and production deployment. The goal was not to use large language models, but to master the foundational, classical ML approach that underpins many real-world AI systems. Additionally, I explored a task-creation workflow that evaluates and tests LLM-based agents across domains; this was new to me, and by experimenting with it, I learned the basics of how complete evaluation tasks are structured and validated end-to-end. During my internship, I worked on the design and implementation of an end-to-end serverless logging pipeline for a production .NET web application, gaining hands-on experience with cloud-native architectures and real-world operational constraints. The 1st part of the project involved migrating application and error logs from an existing MS SQL Server–based logging approach to a fully serverless architecture. I designed and implemented a custom solution using AWS Lambda and DynamoDB, selected for scalability, simplicity, and cost efficiency. The backend .NET application was updated to send HTTP requests to this endpoint. I designed a unified log schema that combined application logs and error logs into a single DynamoDB table, ensuring consistent field structure, JSON serialization, and reliable ingestion through Lambda. The 2nd part of the project focused on log visualization and monitoring. I developed an independent React-based web application that consumes the logging API and displays logs in a structured dashboard. I also implemented dropdown filters, full-text search, and efficient log retrieval for large datasets.WWWW…