● Respond to students’ queries. ● Correct challenges and intermediate deliverables of the final project. ● Follow up with the students: accompany them in their learning process throughout the course, motivate them to meet the challenges and offer support for any difficulties they may encounter

Anyone AI | Apr 2022 - Dec 2022

Machine Learning Engineer

● Object detection for in-store inventory management: Scalable microservices architecture using Docker to implement Flask, Redis, and a fine-tuned model (based on yolov5) trained on AWS EC2 Server with a custom dataset prepared on S3 storage. Ready to deploy with a login security layer to access the API and run inferences. Detects missing object in store shelves and return the image with a Gaussian filter for visualization. ● Products review classification: Analyze sentiment in product reviews for a movie streaming service. Manipulated data that is not in a traditional format, pre-processed it, and vectorized text data using BoW and TF IDF. Trained a word embedding and used it as a vectorization source for the data. Trained a sentiment analysis model to detect positive and negative opinions for movie reviews. ● Image Classification for E-Commerce: Predict vehicle make and model from unstructured e-commerce images. Trained on a pre-built dataset of 196 classes. Visualized and cleaned the dataset, pre-processed and augmented data, and trained a fine-grained classification model using convolutional neural networks achieving 82% accuracy in the prediction of make and model combined. Deployed in AWS instances using Docker, using an API-based web-service application. ● Home Credit Risk Analysis: Predicted whether a person applying for a home credit will be able to repay their debt or not. Manipulated and visualized data, and performed data pre-processing for a large dataset of +350,000 transactions. Trained many supervised models were achieving +0.72 ROC AUC. Models used were DecisionTree, XGBoost, and LightGBM. ● Salary Prediction Model: The goal was to predict salary levels based on historical data for sports players. Collected and analyzed data via an API using Python and Pandas. The original data was unbalanced. Cleaned up data, generated additional fields, stored and created a base dataset. Manipulated and visualized data. Performed feature engineering and standardization. Selected evaluation metrics and baseline models. Trained a linear regression model, achieving an F1 score of 76%. Tech stack: Python, Numpy, SciPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, PyTorch, Docker, Flak, Redis, AWS (EC2 & S3), Bash, Git, OpenCV, Postman

Self-employed | Jan 2014 - Jul 2022

Audio Engineering Consultor

● Designed and deployed audio technology in entertainment and food industries providing consultancy on client's commercial premises and their needs. ● Each project was planned following best practices and recommendations to achieve an optimal acoustic result based on the client’s requirements and physical characteristics.

Self-employed | Jan 2014 - Jan 2022

Mentoring (maths, physics, alegbra)

Prepared +50 students for college admission providing personalized mentoring according to their career goals. From mathematics, algebra, or physics to DELF French preparation

My Education

Take a look at my formal education

Engineering in SoundUniversidad Nacional de Tres de Febrero | 2023

Developer in Artificial InteligenceAnyone AI | 2022

About Me

Machine Learning | Data Scientist | Python Developer | Sound Engineering

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