I’m a Software Engineer graduated from the Faculty of Engineering, University of Buenos Aires (FIUBA).
My background covers both theoretical foundations and practical applications of Software Engineering, with a focus on problem-solving and building robust, scalable systems.
This growing knowledge and skills are substantiated by several projects covering diverse fields of Software Engineering, including:

Understanding of system architectures where components are spread across multiple networked computers. Knowledge in scalability, fault tolerance, and synchronization, essential for large-scale applications.

• GEduNet is an Graphical Educational Computer Network Simulator. It's meant as an aid to students trying to understand computer networks, along with their teachers.
• Horizontally scalable, fault-tolerant distributed system architecture for large-scale data processing in a multicomputing environment, with inter-node communication via RabbitMQ-based message queue middleware.

Proficient in designing and building modern applications and RESTful APIs, applying best practices for clean architecture, maintainability, and efficient communication between services.

• ClassConnect, a (Google Classroom-like) full-stack educational management application that enables seamless interaction between students and teachers.
• Reaserch Page designed to showcase the work of researchers affiliated with CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina).

Strong knowledge of algorithm design and analysis, focusing on efficiency and scalability. Committed to applying software development best practices such as clean code, modular design, and maintainability to ensure high-quality solutions.

Experience in data engineering, data analysis, and visualization to prepare and process raw information into meaningful datasets. Skilled in statistical modeling and the application of Machine Learning techniques, using processed data resulting from the mentioned techniques to build predictive models and support data-driven decision-making.

• Two academic projects demonstrating skills in data manipulation, visualization, and predictive analytics.
• Rohlik Orders Forecasting Challenge, a Kaggle competition focused on predicting the number of Rohlik orders (grocery deliveries) at selected warehouses.

Strong foundations in the design and implementation of abstract data types and data structures, crucial for efficient problem-solving and algorithm development.

• Collection of Abstract Data Types implementations written in pure C. Includes classic structures such as Stack, Binary Search Tree and Hash Table, among others.