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AntonioVitalic/README.md

Hello there! I'm Antonio Vitalic

Computer Science Engineer & Astronomer from Universidad de Chile (FCFM - DCC).

Full Stack Developer at Conergie | Diploma in Generative AI & Language Models from CENIA.


About Me

I hold a degree in Computer Science Engineering and a B.Sc. in Astronomy from Universidad de Chile. My main areas of interest are software engineering, web application development, data analysis, and IT project management. I'm a proactive, organized, and driven person who always gives 100% in every project. I enjoy integrating diverse scientific disciplines and I'm a strong believer that things should be done right — so I'm always learning whatever it takes to get the job done well and fast.


Work Experience

  • Full Stack Developer @ Conergie (Mar 2026 - Present)

    • Building dev projects for an electric supply contract quoting platform.
    • Stack: AWS Lambda, S3, PostgreSQL, Next.js, Tailwind CSS, Django Ninja, React, Vercel.
  • Software Engineer @ CENIA (Mar 2025 - Jul 2025)

    • Built a web platform for managing and optimizing man-hour assignments across CENIA projects.
    • Interactive dashboards for utilization tracking, weekly assignment confirmation, and automatic cost center deductions.
    • Stack: AWS, PostgreSQL, Django REST, React, Vue.
  • Software Engineer Intern @ Haulmer (Jan 2025 - Feb 2025)

    • Migrated 30 endpoints from PHP/Laravel to ASP.NET (C#), achieving 10x performance improvement on average.
    • Full Swagger documentation.
  • Software Engineer Intern @ Pricemaker (Jan 2024 - Feb 2024)

    • Standardized and modularized the web platform for scalability. DB management and migration.
    • Built a proof of concept with Redash and developed a chatbot using the OpenAI API.

Featured Projects

  • Inventario MAPA (Thesis Project) — Full stack platform for the Museo de Arte Popular Americano. Knowledge graph in Neo4j, bulk data import from Excel, Docker deployment. Django REST React Vite TypeScript Neo4j Docker

  • Beauchef Room Reviews — Web app for students to review study rooms at FCFM. Room ratings, filters by building, forum. Python Django MySQL JavaScript

  • Mandelbrot Set Grapher (Wii) — Mandelbrot set renderer for Nintendo Wii / Dolphin emulator.

  • Deep Learning Models — Built neural networks from scratch, CNNs for image classification, and RNN/LSTM models for time series analysis. Python TensorFlow PyTorch


Tech Stack

Languages

Python JavaScript TypeScript C C++ C# Java Scala Go Kotlin Ruby PHP

Frameworks & Libraries

React Next.js Vue.js Angular Django Flask Spring Boot Ruby on Rails Tailwind CSS Node.js

Databases & Cloud

PostgreSQL MySQL Neo4j AWS GCP

Tools

Git Docker VS Code Postman Linux Vercel


Education


Achievements

  • Outstanding Student of Computer Science Engineering, 2023.
  • Regional Chemistry Olympiad finalist, 2016 & 2017.
  • Best student of high school graduating class, 2017.

Languages

  • Spanish: Native
  • English: Advanced (B2 — TOEFL ITP certified)

Contact

LinkedIn Gmail Website


Always open to collaborating on interesting projects and learning new technologies!

Pinned Loading

  1. ResenasSalasBeauchef---Proyecto-de-Ingenieria-de-Software ResenasSalasBeauchef---Proyecto-de-Ingenieria-de-Software Public

    Python

  2. Taller_Desarrollo_Videojuegos_GP1 Taller_Desarrollo_Videojuegos_GP1 Public

    GDScript 1

  3. DesarrolloAppWeb DesarrolloAppWeb Public

    JavaScript

  4. Mandelbrot_Set_Grapher_Wii Mandelbrot_Set_Grapher_Wii Public

    A Nintendo Wii / Dolphin application that renders the Mandelbrot set in real time with interactive controls via the Wii Remote. Built with libogc and devkitPro, it demonstrates dynamic fractal rend…

    Makefile 1

  5. CC3501_Computacion_Grafica CC3501_Computacion_Grafica Public

    Forked from ivansipiran/CC3501

    Fork personal del repositorio del curso CC3501 Modelación y Computación Gráfica para Ingenieros

    Python

  6. CC6204-Deep-Learning CC6204-Deep-Learning Public

    Forked from ivansipiran/CC6204-Deep-Learning

    Fork personal del repositorio del curso CC6204 Deep Learning

    Jupyter Notebook