Portfolio
AI Engineer & Python Developer
RuandersonProfile
AI Engineer focused on Python, Automation, and Artificial Intelligence. I build AI-powered software, scalable APIs, intelligent systems, and human-centered digital products, combining LLM workflows, product thinking, and technical execution to transform complex challenges into real-world impact.
Projetos em destaque
Conteudo vindo do banco Supabase quando configurado.
Plataforma de Pesquisa com EEG e Eye Tracking (LIM - UFPB)
Plataforma full stack de pesquisa que integra sinais de EEG e eye tracking para experimentos sincronizados em IHC e neurotecnologia.
Plataforma de Orquestração de Workflows Multi-LLM (B3)
Plataforma de orquestração para conectar múltiplos LLMs em fluxos integrados, tornando o uso de IA mais prático, modular e escalável.
Drica AI - Sistema de Inteligência para Automação (Softcom)
Solução de automação com IA desenvolvida para clientes da Softcom, analisando bases corporativas e gerando insights acionáveis para diferentes empresas.
Artigos publicados
Analysis of the Enjoyment Levels Evoked by Two Video Commercials Using EEG
This article discusses a research study that investigates how television commercials evoke emotional responses using electroencephalography (EEG) to monitor brain activity. Two commercials were analyzed: one with animated visuals and another featuring live-action actors. The goal was to see if EEG could reflect brain patterns related to viewer enjoyment and purchase intent. The study merges Human-Computer Interaction (HCI) and advertising, emphasizing emotional engagement. Twenty participants watched both commercials under controlled conditions. Questionnaires were given before and after viewing to assess engagement, emotional states, and attitudes toward the content. The findings show a clear preference for live-action videos, though attention decreases as the video continues.
IMPLEMENTAÇÃO DE FERRAMENTAS DE INTELIGÊNCIA ARTIFICIAL NA EDUCAÇÃO: UMA REVISÃO DE ESCOPO
The use of Artificial Intelligence (AI) tools in education has become increasingly prevalent in recent years, particularly following the advancement of generative AI models. Educators have adopted these technologies to automate tasks, create instructional materials, personalize learning activities, analyze open-ended responses, and monitor student learning processes more effectively. Despite this growth, important questions and challenges remain regarding the practical implementation of AI in teaching. This study presents a scoping review that gathers and analyzes recent research on the use of AI by teachers, aiming to understand how these technologies are being applied, the benefits they provide, and the challenges that persist. Eight relevant studies were examined, revealing that AI contributes to personalized learning, improves teaching efficiency by reducing administrative workload, and enhances educators' understanding of students' thinking and learning processes. However, significant limitations were also identified, including insufficient teacher training, ethical concerns, inadequate infrastructure, and difficulties in pedagogical integration. The findings highlight potential pathways for the critical and responsible use of AI in education and emphasize the importance of teacher mediation to ensure that these technologies contribute meaningfully to teaching and learning. These results reinforce the need for aligned educational policies and ongoing support for the effective integration of AI in educational contexts.
Python
FastAPI
Docker
REST APIs
Databricks
Git & CI/CD
Pytest
LangGraph