Andres Fuentes

Andres Fuentes has a diverse work experience in various roles and industries. Andres started their career as an Electrical Engineering Intern at Venezolana de Cementos S.A.C.A. in 2018, where they conducted a project on the reliability evaluation of an electrical power plant in a cement plant. In 2019, they worked as a Project Engineer at Ingeniería Cardón (IC), where they developed a relational database and performed electrical and mechanical characterization of structures.

From 2020 to 2021, they worked as a Data Scientist at Freelancer.com, where they successfully developed a drone pilot detection system and a face detection system using machine learning and deep learning techniques. In 2021, they worked as a React Developer at TowerHouse Studio, focusing on bug fixing in front-end applications.

In 2021, Andres worked as a Cloud Developer at Arkhotech, specializing in cloud migration and implementing disruptive solutions. Andres developed data transformation pipelines using ETL jobs with technologies like Spark, Pandas, and Hadoop. From 2022 to 2023, they worked as a Data Engineer at Kognitions, where they developed data transformation pipelines and monitoring systems for data-driven innovation ecosystems.

Currently, Andres is working as a DevOps Engineer at TIMining, starting in 2023. The details of their responsibilities and achievements in this role are not provided.

Andres Fuentes obtained an Engineer's Degree in Electrical and Electronic Engineering from Universidad de Oriente (VE) from 2014 to 2019. In addition, they have obtained various certifications including AWS Certified Data Analytics - Specialty, AWS Certified Developer - Associate, and AWS Certified Cloud Practitioner from Amazon Web Services (AWS). Andres has also completed several online courses on Coursera, including Sequences, Time Series and Prediction, DeepLearning.AI TensorFlow Developer Specialization, Convolutional Neural Networks in TensorFlow, Introduction to Power Electronics, Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, Deep Learning Specialization, Sequence Models, Convolutional Neural Networks, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Projects, Neural Networks and Deep Learning, Machine Learning, and Solar Energy Basics.