Giovani Moctezuma Rodríguez León, Developer in Santiago de Querétaro, Mexico
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Giovani Moctezuma Rodríguez León

Verified Expert  in Engineering

Artificial Intelligence (AI) Developer

Santiago de Querétaro, Mexico
Toptal Member Since
November 27, 2020

Giovani is a software engineer specializing in artificial intelligence, machine learning, and data science tech stacks. He has worked in multicultural teams for startups and big enterprises, implementing data analytics, machine learning, and deep learning in the transportation, retail, job search, and supermarket sectors. As a freelancer and an entrepreneur, Giovani is creating his own set of solutions using facial recognition and computer vision.


Scikit-learn, Keras, TensorFlow, OpenCV, C#.NET WinForms, Python...
Online Job Search Company
Machine Learning, Recommendation Systems, Python, Amazon Web Services (AWS)...
Systems Experts
Data Science, Machine Learning, Deep Learning, Python, Computer Vision, OpenCV...




Preferred Environment

Keras, Scikit-learn, TensorFlow, Git, Visual Studio Code (VS Code), Ubuntu, Python

The most amazing...

...thing I've designed, developed, and optimized is a matching algorithm that went from two minutes to 10 seconds per prediction.

Work Experience


2019 - PRESENT
  • Built a customized QR code-like solution for a customer's internal management system.
  • Developed customer segmentation on a dataset of two million records for a large grocery store.
  • Designed and developed an algorithm to predict crime occurrence and firearm collection in a major city in Latin America.
  • Built a complete solution to automate entrances and exits at sports centers, using facial recognition technology.
Technologies: Scikit-learn, Keras, TensorFlow, OpenCV, C#.NET WinForms, Python, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Computer Vision, Neural Networks, Data Visualization, Data Science, Machine Learning, Deep Learning

Machine Learning Engineer

2018 - 2019
Online Job Search Company
  • Implemented an ETL pipeline from scratch to process the full-site database.
  • Designed and developed a customized matching algorithm that makes predictions two orders of magnitude faster.
  • Set up Apache Solr to complement the matching capabilities of my algorithm.
  • Mixed in-house algorithms with third-party services like IBM Watson to enhance matching results.
  • Followed coding best practices during Agile development cycles.
Technologies: Machine Learning, Recommendation Systems, Python, Amazon Web Services (AWS), Natural Language Understanding (NLU), IBM Watson, MySQL, ETL, NumPy, Apache Solr

AI Engineer

2017 - 2018
Systems Experts
  • Developed an algorithm to identify passengers' entrances and exits for a nationwide transportation enterprise, thereby reducing losses by about 10%.
  • Applied object detection techniques to ensure quality in a product presentation for a nationwide food chain.
  • Implemented neural networks and classical computer vision approaches, using TensorFlow and OpenCV.
  • Developed fast-prototyped presentation demos within two to three weeks.
  • Worked with Agile methodologies and on-site source control to ensure confidentiality.
Technologies: Data Science, Machine Learning, Deep Learning, Python, Computer Vision, OpenCV, Linux, Object Detection, TensorFlow


2017 - 2017
Carso Research and Development Center
  • Designed an autonomous monitoring system that uses drones for surveillance in industrial complexes and buildings. Focused on providing a solution that's low price and easily replaceable.
  • Implemented raw GPS metrics on low-level interfaces and code to outperform conventional position measurements.
  • Developed a prototype that costs 70% less than similar solutions in the market.
  • Designed and implemented a complete initial prototype within one month.
Technologies: Raspberry Pi, Linux, Wireless Protocols, Mechatronics, Sensor Fusion, Computer Vision, GPS, Drones

Face Recognition POC for Arizona State University

FaceMatch is a proof of concept for a face-based identification system developed as a Third Horizon Initiative within the university technology office at Arizona State University. I developed and set up all the back-end code and infrastructure, using Python and AWS tools.

Python SDK for Data Labeling Startup (RedBrickAI)
A public package and open-source Python software development kit for a data labeling company to seamlessly integrate with their back-end infrastructure. I contributed to the development of new features, testing, and documentation while following coding best practices.

AI-powered Job Search Site

A full site for job search focused on IT. As the machine learning engineer, I designed and developed an automated matching algorithm that combines on-site algorithms with third-party tools like IBM Watson to enhance performance. The matching feature was a big differentiator against competitors.

The redesign and implementation of the matching algorithm produced results two orders of magnitude faster, from two minutes to less than 10 seconds per prediction. The biggest challenge was joining data from different sources and third-party APIs to produce high-quality predictions.

Face ID for Sport Centers

An offline, machine learning-powered app to automate entrances and exits at sports centers. I designed and developed the whole solution, using CNN-based algorithms for face recognition on the back end with a WinForms UI as the front end.

The software helped to manage sports center partners and eliminate losses due to pending payments or expired memberships. The solution was capable of recognizing people with 99% accuracy (based on public datasets).

Data Analysis and Insight Extraction for a Retail Store

An in-depth study of consumer trends for a retail store. As the primary data scientist, I performed customer segmentation techniques using clustering and data visualization to extract powerful insights. This involved analyzing millions of records to identify trends based on customer demographics and customer behavior such as buying patterns.


Python, SQL, GraphQL


Machine Learning, Deep Learning, Artificial Intelligence (AI), Computer Vision, Recommendation Systems, Natural Language Processing (NLP), Software Engineering, Mathematical Modeling, Object Detection, Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs), Facial Recognition, GPT, Generative Pre-trained Transformers (GPT), Natural Language Understanding (NLU), Data Visualization, Feature Analysis, Linear Algebra, Unsupervised Learning, Data Analysis, Drones, GPS, Sensor Fusion, Mechatronics, Wireless Protocols, Gunicorn, API Documentation, HTTPS, Open Source


TensorFlow, Scikit-learn, OpenCV, PyTorch, Keras, NumPy, Pandas, REST APIs, Flask-RESTful, Dlib


C#.NET WinForms, Git, Apache Solr, IBM Watson, Jupyter, TensorBoard, NGINX, GitHub, PyPI, Travis CI, Pytest


Data Science, Object-oriented Programming (OOP), ETL


Linux, Jupyter Notebook, Ubuntu, Amazon Web Services (AWS), Raspberry Pi, Amazon EC2, Visual Studio Code (VS Code)





2013 - 2018

Bachelor's Degree in Computer Science

Technological Institute of Queretaro - Queretaro, Mexico

2017 - 2017

Bachelor's Degree in Computer Science (Study Abroad)

West Virginia University - Morgantown, WV, USA


TensorFlow: Data and Deployment

DeepLearning.AI (via Coursera)


TensorFlow Developer

DeepLearning.AI (via Coursera)

APRIL 2018 - APRIL 2021

HCNA Routing & Switching

Huawei ICT Academy

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