About Me

Hi there! 👋 I’m Dylan

I’m a passionate Systems Engineer from Argentina 🇦🇷, deeply in love with Mathematics and endlessly fascinated by Artificial Intelligence. Currently working as a Data Scientist specializing in Computer Vision and Natural Language Interfaces, my goal is to build impactful, scalable AI systems that make real-world operations smarter.

📈 Career Timeline

Throughout my journey, I have progressively deepened my expertise in Artificial Intelligence and Machine Learning, moving from academic research in optimization algorithms to building real-world AI applications. My experience spans Computer Vision, LLM-based agentic systems, and scalable model deployment — bridging theory and practice to deliver innovative solutions.

🚀 Selected Projects

I’ve worked on several open-source projects (I love open source!), both to apply my knowledge and to solve real-world problems. Here are a few of my favorite projects that showcase my passion for AI, automation, and building practical solutions:

UdemyPy — Free Udemy Courses Bot

Developed a web-scraping system to aggregate free Udemy courses from multiple platforms, automating data extraction with high reliability.
Integrated Telegram and Twitter APIs to deploy real-time notifications, growing the Telegram channel to over 5,700 subscribers.
Achieved over 235 GitHub stars, demonstrating strong adoption and community engagement.
Containerized the full application using Docker for streamlined deployment across diverse environments.

Kitten Mixer — Create unique kitten images

Designed and trained a custom Variational Autoencoder (VAE) in PyTorch to generate novel kitten image blends.
Built a modular Python package with configurable architectures, custom beta-loss functions, and checkpointing systems.
Deployed an interactive web application using Streamlit, achieving high model responsiveness and usability.
Achieved strong user engagement through the project website and blog, showcasing ML modeling and productization.

🛠️ My Technical Skills

Languages/Tools: Python, SQL, Docker, Git, Azure SDK, OpenCV, Ablumations, Fast API.
Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn, Pandas, NumPy, LangChain, LangGraph, HuggingFace.
Machine Learning: Classification (Random Forest, KNN, SVM, Convolutional Neural Networks), Regression Modeling (linear, logistic), Object detection, Object segmentation, Large Language Models (LLMs).