Gabriel Cavalcante

Software Engineer — Frontend, AI/ML & Cloud

Email: gabriel.lcifba@gmail.com
Phone/WhatsApp: +55 (74) 98134-3313
LinkedIn: linkedin.com/in/zolppy
GitHub: github.com/zolppy

Professional Summary

Skilled frontend developer and AI enthusiast with expertise in React.js, Next.js, TypeScript, and AWS. Experienced in building responsive UIs and implementing AI solutions, he holds certifications in Gen AI and Agile methodologies. Fluent in Portuguese and English, he is passionate about innovation and continuous learning in tech.

Skills

Accesibility (WCAG), Agile, Artificial Neural Networks, AWS Cloud Computing, Boto3, Computer Vision, Convolutional Neural Networks, CSS3, Docker, Express.js, FastAPI, Framer Motion, Generative AI, Git, GitHub, HTML5, Jest, JavaScript, Kanban, Keras, LangChain, Linux, Machine Learning, Next.js, Node.js, Performance Optimization, PostgreSQL, Python, React Testing Library, React.js, Rest APIs, Retrieval-Augmented Generation, Scrum, SQL, Tailwind CSS, TensorFlow, TypeScript.

Professional Experience

Desenvolvedor Frontend

Eruda — Remote · Jacobina, BA
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  • Developed and maintained modern, fluid, and accessible user interfaces using cutting-edge technologies.
  • Ensured dynamic, performant, and responsive designs aligned with high-fidelity models.
  • Collaborated with cross-functional teams to deliver seamless user experiences.

Artificial Intelligence and Machine Learning Intern

Compass UOL — Remote · Passo Fundo, RS
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  • Completed an extensive learning path covering NLP, Artificial Neural Networks, Deep Learning, and AWS Cloud Computing.
  • Implemented AI models for real-world applications, including chatbots and optical character recognition (OCR).
  • Worked on practical projects to apply theoretical knowledge in AI and cloud technologies.

Technical Support and Integration

Infotec — In Person · Jacobina, BA
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  • Assembled, maintained, and configured computers and peripherals.
  • Managed system integrations, including network and database configurations.
  • Provided technical support to ensure seamless operations and system reliability.

Programa Institucional de Bolsas de Iniciação Científica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) — Hybrid · Jacobina, BA
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  • Prepared lesson plans, teaching materials, and pedagogical resources for Computer Science education.
  • Conducted workshops and other teaching-related activities to support student learning.
  • Contributed to academic research and development in the field of Computer Science.

Education

Degree in Computer Science

Instituto Federal de Educação, Ciência e Tecnologia da Bahia (IFBA), Jacobina — 2019 — Now.

Certifications

  • Gen AI Technical Certified — (2024)
  • AI-Assisted Certified Professional — (2024)
  • Agile-Certified Fast Inception with AI Cockpit — (2024)

Projects

Interior Design

Responsive, modern and fluid langing page that shows interior design projects, developed using cutting-edge technologies and methodologies like Next.js, Tailwind CSS, TypeScript, Git, Github, Docker, Scrum and mobile first.

Fashion Blog

Responsive, modern and fluid fashion blog that shows interior design projects, developed using cutting-edge technologies and methodologies like Next.js, Tailwind CSS, TypeScript, Git, Github, Docker, Scrum and mobile first.

Then Crust Pizza

Responsive, modern and fluid langing page that shows kind of pizza in sale, developed using cutting-edge technologies and methodologies like Next.js, Tailwind CSS, TypeScript, Git, Github, Docker, Scrum and mobile first.

Image Dimensionalization Reduction

A Python project for converting RGB images to grayscale and binary formats. Uses luminance calculation for grayscale conversion and thresholding for binary conversion. Includes functions to save the processed images. Built with NumPy and PIL (Python Imaging Library).

Iris Classifier

This project demonstrates the use of a Multi-layer Perceptron (MLP) classifier on the Iris dataset. It includes data preprocessing, hyperparameter tuning with GridSearchCV, model evaluation using metrics like accuracy, precision, recall, F-score, and specificity, and visualization of ROC curves for multi-class classification.

Cats and Dogs Classifier

Implementation of two models using a convolutional neural network (CNN) to classify images into two distinct classes (dogs and cats). Transfer learning is performed between the first and second models to improve results, which are measured using performance metrics to assess training progress.

Languages

  • Portuguese (native)
  • English (advanced)