Alejandro Puig
Consultor Independiente at AMP Tech
Greater Mexico City, State of Mexico
Summary
👤
Senior🎓
Top SchoolAlejandro Puig is an AI-focused consultant and practitioner with eight years of experience building and scaling machine learning solutions across industry and consulting engagements. He led AI strategy for consumer analytics at PepsiCo LATAM and has hands-on expertise in deep learning for images, audio, sensors and tabular data through his independent work at AMP Tech. Alejandro combines technical depth—master’s training in AI, Udacity specializations in deep reinforcement learning and practical TensorFlow work on neural networks such as MNIST—with experience advising organizations on ML maturity and implementation. He also actively disseminates knowledge via a YouTube channel and podcast, bringing research and production perspectives to broader audiences. Based in Greater Mexico City, he blends enterprise leadership with creator-driven outreach, making complex AI topics accessible to practitioners and decision makers.
9 years of coding experience
5 years of employment as a software developer
Ingenieria en Sistemas, Ingenieria en Sistemas at Universidad AnáhuacGrado, Deep Reinforcement Learning, Grado, Deep Reinforcement Learning at UdacityMaster's degree, Ciencia e Ingeniería de la Computación | Inteligencia Artificial, Master's degree, Ciencia e Ingeniería de la Computación | Inteligencia Artificial at Universidad Nacional Autonoma
Spanish, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
neural-network10
machine-learning10
tensorflow10
python10
mnist10
data-analysis10
keras9
Programming languages (3)
Github contributions (5)
puigalex/AMP-Tech
Aug 2017 - Jul 2020
Role in this project:
Data Scientist
Data ScientistContributions:20 commits, 1 PR, 19 pushes in 2 years 10 months
Contributions summary:Alejandro contributed to the development of machine learning models within the repository. Their work focused on building and training neural networks, demonstrated through the implementation of various models like PCA, and deep neural networks. The user also experimented with datasets and employed frameworks such as TensorFlow and TFLearn for the development and evaluation of these models. These contributions are geared towards number recognition tasks, specifically MNIST.
puigalex/deteccion_objetos
Feb 2019 - Feb 2019
Contributions:1 commit, 1 branch in 1 day
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