I have an extensive repertoire of over 450 Python scripts, focused on competitive programming, web development, automation, API consumption and creation, data engineering, IoT, and the use of artificial intelligence for various tasks.

I have used Python to automate tasks such as file manipulation and data processing, creating scripts that enhance efficiency in repetitive processes, like an RPA for comfortable listening of YouTube playlists. I have also developed data analysis projects, like an anime recommendation system based on the MyAnimeList API, developed solutions to many competitive programming problems in Codewars, and participated in game development, notably creating a Dungeons & Dragons game generator with interactive map and AI usage for image generation and natural language processing, my biggest and coolest project to date, which I'm going to update soon to introduce better practices, a better map engine, and CI/CD.

Additionally, I have worked on web application development using Django, including a movie website, a numeric analysis and linear regression interactive graphicator, an enterprise data interactive graphicator and a tech learning route generator that is a project we made for SoftServe Inc., a real client. With non-opinionated frameworks, I made an Online Store backend using Flask (the usual, but with swagger, smorest, migrations and JWT), also a P2P Chord-based network, and a manual Hadoop Distributed Filesystem (HDFS). Also on framework-less applications, like a shortest route calculator/graphicator system for Medellín's streets, with a Medellín's Metro version developed along Omdena Inc., an international team.

In the field of artificial intelligence, I have implemented models for facial and voice recognition, and I have also explored home automation with a smart curtain system (RaspberryPI) that responds to external light, and for Data Engineering i've worked on a Covid data consumption and analysis with all the AWS and PySpark stack, which I learnt deeper in Globant as a tech trainee in my professional internship.

My experience with Python is diverse, focusing on creating practical and innovative solutions while solving logical challenges that I find satisfying.

In my current role at Globant, I am part of the Index/RAG team, where we develop AWS-based infrastructure using Python Lambda functions and CloudFormation stacks to integrate OpenSearch and Azure AI Search, enhancing the retrieval capabilities of AI agents.

I design and implement multimodal data processing pipelines that handle diverse document types, including a multilingual OCR system built with pytesseract and tesseract, supporting multiple formats and languages. These components are modular and composable, enabling their reuse across multiple infrastructures within the client’s platform.

Alongside my core RAG work, I build FastAPI-based crawler agents that aggregate information from Confluence, Jira, Azure DevOps, and other APIs, indexing and structuring it for intelligent retrieval. I also mentor teammates in building similar integrations for GitHub, Bitbucket, and Salesforce, promoting modular and scalable design patterns across the team.

Beyond these integrations, I contribute to developing in-situ Python agents that process documents directly within the infrastructure—such as OCR pipelines or data transformers—providing reusable, low-latency solutions for AI-powered applications.

Beyond professional work, I co-founded and led a Competitive Programming Club with close friends, where we spent years studying and experimenting with advanced algorithms, optimization strategies, and data structures. We focused especially on probabilistic structures and specialized sorting algorithms, which became my personal favorites.

Our club fostered deep algorithmic thinking and collaboration, and we actively shared our insights and findings on LinkedIn. Some of these discussions can be seen on my profile, particularly this post about the value of competitive programming, which captures part of what we built together.

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