• 🎓 Computer Science student at Escuela Superior Politécnica del Litoral (ESPOL).
• 💻 Interested in software development, backend engineering, and software testing.
• 🧪 Exploring quality assurance through manual and automated testing.
• 🐧 Experienced in academic and personal projects involving Linux, Docker, databases, and distributed systems.
• 🚀 Currently strengthening my portfolio through full-stack development and testing projects.
• 🤝 Volunteer at IEEE Computer Society – ESPOL.
• 🌎 Based in Ecuador and open to learning, collaboration, and professional opportunities.

Here are some of the projects I've worked on, combining software development, systems engineering, artificial intelligence, and software testing.

A full-stack web application designed to manage software projects, track defects, organize test cases, and record test executions.

The platform includes role-based authentication, defect lifecycle management, automated testing, and an explainable rule-based bug prioritization service.

Technologies: React, TypeScript, Node.js, Express, PostgreSQL, Python, FastAPI, Docker, Playwright, Vitest, GitHub Actions.

Key Features:

• Project and defect management.
• Role-based authentication and authorization.
• Test case and test execution management.
• Bug history and traceability.
• Explainable bug priority suggestions.
• Automated API and end-to-end testing.
• CI/CD pipeline with GitHub Actions.

📂 View Repository

A distributed monitoring solution designed to collect and analyze Linux system logs and container resource metrics.

The project combines systems programming, process communication, containerization, and monitoring technologies to detect anomalies and visualize system activity.

Technologies: C, Python, Linux, Docker, systemd, journalctl, Prometheus, PostgreSQL, Nginx.

Key Features:

• Multi-process log monitoring.
• Linux service monitoring through journalctl.
• Process communication using pipes.
• Log classification by priority.
• CPU, memory, and disk metrics.
• Containerized monitoring environment.
• Anomaly detection and alert generation.

📂 View Repository

A computer vision project focused on detecting and quantifying vehicles in urban traffic intersections using convolutional neural networks.

The system processes traffic images and predicts vehicle locations, bounding boxes, and detection confidence.

Technologies: Python, PyTorch, Computer Vision, Deep Learning, ResNet18.

Key Features:

• Vehicle detection using convolutional neural networks.
• ResNet18-based feature extraction.
• Bounding box prediction.
• Object detection and visualization.
• Model training and evaluation.

📂 View Repository

I'm currently focused on expanding my knowledge and gaining practical experience in:

• Software Development & Backend Engineering.
• Software Testing & Quality Assurance.
• Test Automation.
• APIs and Database Management.
• Building reliable and maintainable applications.

My goal is to contribute to real-world technology projects while continuing to grow as a software engineer.

I'm always open to connecting with other developers, learning from new experiences, and exploring professional opportunities.