I'm a highly skilled mathematician who has transitioned into a successful career as a Data Scientist and Machine Learning Engineer over the past five years. My Master's degree in Mathematics has equipped me with a solid foundation in problem-solving. I'm deeply immersed in everything from data engineering to MLOps, enthusiastically pushing the envelope with Python and an extensive array of technologies such as GCP, Kubernetes, MLflow, Kubeflow, Hugging Face, and Langchain. At Globant, I've recently been focused on leveraging Kubeflow to orchestrate the lifecycle of Machine Learning models for a client, ensuring that each phase, from training to deployment and model monitoring, is executed seamlessly and efficiently. Additionally, I am developing innovative solutions using Large Language Models. As part of my role as a lecturer at EAFIT, I've been teaching graduate courses such as "Applied Machine Learning" and "Statistical Learning" within the Data Science master's program, molding the minds of the next generation of data professionals.