I have been working as a Fullstack Developer for 4 years, building complete solutions from backend to frontend, focusing on performance, security, and efficient design.
I enrolled in the Software Engineering program at the University of Brasília. During this period, I primarily developed applications for academic use. One notable project was integrating the “Guardiões da Saúde” app—which tracked potential COVID-19 exposures in real time—with a Telegram chatbot, enabling users to report their symptoms without installing an external app.
A chatbot developed in Flask that integrated with the Guardiões da Saúde app to track Covid-19 symptoms in real-time, using Firebase as a database and the Telegram API for communication.
View CodeIn 2020, I earned several important certifications:
I began an internship at DBS Web as a fullstack web developer. During this time, I developed and deployed a time tracking application for employees and was responsible for the security and backend of various client projects.
An electronic time tracking system developed with Flask, using Firebase for authentication, MySQL for data storage, and Redis for performance optimization and real-time shift tracking.
View CodeIn July 2023, I transferred from the Software Engineering program at the University of Brasília to the Computer Science program at the Universidade Católica de Brasília. Around the same time, I began working as a fullstack freelance developer.
I developed a course management system (LMS) for Instituto Panapaná, a platform dedicated to providing free education for young people in vulnerable situations, prioritizing accessible learning opportunities over traditional administrative enhancements.
A LMS designed for Instituto Panapaná, focused on delivering free educational resources to young individuals in vulnerable circumstances, emphasizing accessible learning rather than conventional academic administration.
View CodeI am currently developing AILib, a system built with Electron.js and React that leverages AI APIs to enhance the reading and annotation of books. Key features include ultra-detailed book summaries, advanced Retrieval-Augmented Generation (RAG) with QDrant as the vector database, and parallelized document processing—capable of embedding up to 500 pages per minute into a SQLite database, that handles the Parent Document Retrieval with QDrant.
A system built with Electron.js and React that leverages AI APIs to enhance the reading and annotation of books. Key features include ultra-detailed book summaries, advanced Retrieval-Augmented Generation (RAG) with QDrant as the vector database, and parallelized document processing—capable of embedding up to 500 pages per minute into a SQLite database, that handles the Parent Document Retrieval with QDrant.
View CodeI've worked on developing a digital content generation system for clients. This application generates text using large language models (LLMs), automatically curates relevant news for each client, and produces AI-generated audio and video content. The system is built with Next.js on the frontend, FastAPI on the backend, employs REDIS for real-time polling via callbacks, uses MySQL for data storage, and leverages QDrant as a vector database.
A web application for creating automated content using large language models (LLMs). The app curates news, generates text, and produces AI-generated audio and video content. Developed with Next.js, FastAPI, MySQL, and Redis, the system leverages QDrant as a vector database for efficient data indexing.
View CodeBuilding RAG.NET has been an exciting challenge. From designing a flexible workflow structure to implementing advanced AI-driven processing techniques, every step has required balancing performance and modularity. The system empowers users to tailor their RAG pipelines with cutting-edge chunking, querying, and ranking methods. As development progresses, I’m expanding integrations with new embedding and conversation providers, pushing the boundaries of what’s possible in intelligent retrieval systems.
A modular system built with ASP.NET Core, Angular 19, PostgreSQL, and QDrant, designed to create and execute powerful RAG workflows with customizable AI components.
View CodeI have a diverse skill set that spans backend, frontend, databases, and other complementary technologies. Explore my proficiencies in various technologies and tools that power my projects.
This section showcases my backend development expertise, focusing on building robust, scalable, and secure systems. It highlights my proficiency in programming languages, frameworks, and technologies that power server-side logic and data management.
This section demonstrates my expertise in crafting engaging and responsive user interfaces. I combine modern frameworks and design principles to deliver seamless, interactive, and visually appealing front-end experiences.
My experience with databases spans from traditional relational systems to modern NoSQL and vector-based solutions. I design and optimize database schemas, ensuring data integrity, performance, and scalability across various projects.
Beyond core development, I excel in integrating complementary technologies and tools that enhance security, deployment, and collaboration across projects.
A modular system built with ASP.NET Core, Angular 19, PostgreSQL, and QDrant that enables the creation of custom Retrieval-Augmented Generation (RAG) workflows. Users can configure Chunkers, Query Enhancers, Filters, and Rankers, integrating technologies like Proposition Chunking, Auto Querying, Self Querying Retrieval, and CoHere Reranker.
A system built with Electron.js and React that leverages AI APIs to enhance the reading and annotation of books. Key features include ultra-detailed book summaries, advanced Retrieval-Augmented Generation (RAG) with QDrant as the vector database, and parallelized document processing—capable of embedding up to 500 pages per minute into a SQLite database, that handles the Parent Document Retrieval with QDrant.
A web application for creating automated content using large language models (LLMs). The app curates news, generates text, and produces AI-generated audio and video content. Developed with Next.js, FastAPI, MySQL, and Redis, the system leverages QDrant as a vector database for efficient data indexing.
An IP tracking application that allows users to search for geolocation information of IP addresses. Developed with Angular, the application utilizes a mapping library to display the location and makes requests to geolocation APIs to obtain accurate data.
A Kanban-style task management system built with Next.js and MongoDB, using React Query for efficient state synchronization between client and server.
An application built with Next.js, Tailwind CSS, and MongoDB to store and share developers' social media links, with authentication and profile visibility options.
Landing page for a law firm, developed with Next.js and TailwindCSS. The site includes a contact form integrated with ReSend for email sending.
Website for a psychology clinic, featuring an appointment booking system and authentication with multiple permission levels. Developed with Next.js, TailwindCSS (ShadCN), and MongoDB.
An electronic time tracking system developed with Flask, using Firebase for authentication, MySQL for data storage, and Redis for performance optimization and real-time shift tracking.
A chatbot developed in Flask that integrated with the Guardiões da Saúde app to track Covid-19 symptoms in real-time, using Firebase as a database and the Telegram API for communication.