Hazem Essam
Machine Learning Engineer at Proteinea
Alexandria, Alexandria, Egypt
Summary
👤
Senior🎓
Top SchoolHazem Essam is a Machine Learning Engineer with 11 years of software engineering experience, currently applying deep learning research and production practices at Proteinea in Alexandria, Egypt. He blends academic rigor from a Computer Science MS program with hands-on engineering, having previously built backend systems for e-commerce at Uniparticle and taught core CS courses as a graduate teaching assistant. Hazem contributes to prominent open-source ML tooling—adding audio dataset utilities, TensorFlow I/O integration, and tests to the widely used Keras project—demonstrating expertise in audio data pipelines and model-ready dataset engineering. He is comfortable across the ML lifecycle, from data ingestion and preprocessing to model training and integration into production systems. Colleagues describe him as a pragmatic engineer who brings research-grade solutions into practical deployments while keeping an eye on maintainability and testability.
10 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Arab Academy for Science, Technology and Maritime TransportMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at The American University in Cairo
Arabic, English
Github Skills (9)
machine-learning10
audio-processing10
tensorflow10
python10
data-engineering9
neural-network9
deep-learning9
data-science8
pytorch3
Programming languages (4)
Github contributions (5)
keras-team/keras
Apr 2022 - Apr 2022
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:8 reviews, 33 commits, 7 PRs in 10 days
Contributions summary:Hazem primarily focused on developing and integrating audio dataset utilities within the Keras framework. Their contributions include the creation of an `audio_dataset.py` file, which provides functions for loading audio data from directories and preparing it for model training. Furthermore, the user added TensorFlow I/O integration and unit tests for the audio dataset functionalities, along with implementing support for both fixed-length and ragged datasets.
pythondata-sciencedeep-learningneural-networksmachine-learning
hazemessamm/kyosai
Feb 2021 - Oct 2022
Contributions:124 commits, 115 pushes in 1 year 8 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.