Machine Learning Engineer2021 - PRESENTG42
Technologies: PyTorch, Python 3, Solidity, You Only Look Once (YOLO), Game Theory, Computer Vision, Object Tracking, Chatbots, Rasa NLU, Moralis, Artificial Intelligence (AI), Ubuntu, Microsoft Excel, Jira, Agile Software Development, Machine Learning, Random Forests, Linear Regression, Logistic Regression, PyCharm, XGBoost, Scikit-learn, Sublime Text 3, Sublime Text, Video Processing, Image Processing
- Delivered a Computer Vision project to automatically read ID documents scans.
- Developed Computer Vision software to detect road traffic violations.
- Delivered a PCR registration chatbot, the basis was used to create other services later on.
Computer Vision Developer2019 - 2020MindsLenses
Technologies: Amazon Web Services (AWS), Deep Learning, Python, AWS, PyTorch, SQL, PostgreSQL, Machine Learning
- Created a pipeline that processes everyday movies collected from RPI cameras. The processing includes several deep learning algorithms that detect human emotions in association with product interactions that are being handled in the store.
- Trained deep learning models for object detection and classification. Utilized state-of-the-art frameworks to produce the best quality predictions.
- Managed stable process of videos from raspberry pies to CV inference on the AWS cloud.
Machine Learning Engineer2017 - 2019Sportradar US
- Created a neural network for the NFL to generate drive outcome predictions to further use from the betting companies.
- Built NBA shot probability neural network models using Pytorch for fan experience enhancement.
- Identified NBA events on games using tracking features with XGBoost.
- Implemented an NBA data pipeline from different data sources.
- Created an NBA similarity model for draft players for fan enhanced experience.
Data Scientist2016 - 2017Alem Research
Technologies: Java, Python
- Scraped 10 million company front pages from a social network with Java.
- Created a sentiment analysis neural network model using TensorFlow to label various English texts (including articles, comments, and more) as positive, negative, or neutral.