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I am an AI/ML Engineer with a Master’s degree in Human Language Technology from the University of Arizona and a background in Electrical Engineering from La Universidad Tecnológica de Pereira, Colombia. Specializing in natural language processing, data pipelines, and applied machine learning. My work focuses on building end-to-end systems that transform large, complex, and often unstructured data into structured, actionable insights, with an emphasis on scalability, data quality, and real-world applicability.

My interest in this field began during the last year of my undergraduate studies in Electrical Engineering in Colombia, where I was first exposed to classifiers, machine learning, and neural networks. This experience sparked a lasting interest in how computational methods can model, process, and extract meaning from real-world data.

During my Master’s program in Human Language Technology at the Linguistics Department, I developed a strong foundation in statistical natural language processing and quantitative methods for linguistic data. I worked with core NLP techniques such as n-gram modeling, document classification, and information retrieval, and applied statistical methods including regression modeling, hypothesis testing, and mixed-effects modeling.

During my internship at Goods Unite Us, I developed scalable data pipelines integrating multiple data sources, including SEC filings, web-based data, and company datasets. My work involved extracting executive information, resolving company identities, and linking corporate data to external sources, contributing to the development of structured datasets used for analyzing corporate and political behavior.

In addition to industry experience, I have conducted research involving multilingual corpora, statistical modeling, and machine learning, including building annotated datasets and designing psycholinguistic experiments using eye-tracking and self-paced listening.

My technical toolkit includes Python (pandas, NumPy, scikit-learn, NLTK, spaCy, Transformers, PyTorch, TensorFlow), R (tidyverse, ggplot2, quanteda), SQL, and cloud platforms. I am particularly interested in solving real-world problems where data is messy, ambiguous, and large-scale.

I am currently seeking full-time opportunities in Artificial Intelligence, Machine Learning, or Data Analytics where I can contribute to building data-driven systems that create measurable impact.

You can also view my work on GitHub or connect with me on LinkedIn!