Giovani Moctezuma Rodríguez León, Artificial Intelligence (AI) Developer in Santiago de Querétaro, Mexico
Giovani Moctezuma Rodríguez León

Artificial Intelligence (AI) Developer in Santiago de Querétaro, Mexico

Member since October 29, 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.
Giovani is now available for hire

Portfolio

  • QuetzAI
    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, Android Development...

Experience

Location

Santiago de Querétaro, Mexico

Availability

Part-time

Preferred Environment

Keras, Scikit-learn, TensorFlow, Git, 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.

Employment

  • Founder

    2019 - PRESENT
    QuetzAI
    • 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), 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, Amazon S3 (AWS S3), AWS, 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, Android Development, Augmented Reality (AR), Computer Vision, OpenCV, Linux, Object Detection, TensorFlow
  • Intern

    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

Experience

  • 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)
    https://github.com/redbrick-ai/redbrick-sdk

    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.

Skills

  • Languages

    Python, SQL, GraphQL
  • Other

    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, Recurrent Neural Networks, Facial Recognition, AWS, 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
  • Libraries/APIs

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

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

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

    Linux, Jupyter Notebook, Ubuntu, Amazon Web Services (AWS), Raspberry Pi, Amazon EC2
  • Frameworks

    Flask
  • Storage

    MySQL, SQLite, JSON

Education

  • Bachelor's Degree in Computer Science
    2013 - 2018
    Technological Institute of Queretaro - Queretaro, Mexico
  • Bachelor's Degree in Computer Science (Study Abroad)
    2017 - 2017
    West Virginia University - Morgantown, WV, USA

Certifications

  • TensorFlow: Data and Deployment
    AUGUST 2020 - PRESENT
    DeepLearning.AI (via Coursera)
  • TensorFlow Developer
    AUGUST 2020 - PRESENT
    DeepLearning.AI (via Coursera)
  • HCNA Routing & Switching
    APRIL 2018 - APRIL 2021
    Huawei ICT Academy

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