Andrew Khalel
Software Development Engineer at Amazon
Madrid, Community of Madrid, Spain
Summary
🤩
Rockstar🎓
Top SchoolAndrew Khalel is a Software Development Engineer with 11 years of experience currently building search capabilities for Amazon's Books Search team from Madrid. He blends a strong academic foundation (MS and BE in Computer Engineering, high honors) with applied machine learning and computer vision expertise gained in research and industry roles, including internships at Inria and Valeo. Andrew has contributed to open-source reinforcement learning tooling—extending Tensorforce with ViZDoom and OpenSim environments—demonstrating an ability to integrate diverse simulation platforms and fix hard-to-reproduce bugs. His background spans satellite imagery analysis, multi-task learning for pan-sharpening and segmentation, and production-focused CV work at a startup, showing versatility from research to shipping features. Colleagues would describe him as hardworking and fun-loving, comfortable switching between deep technical problems and pragmatic engineering trade-offs. He brings a track record of turning academic research into practical systems that scale in real-world products.
11 years of coding experience
3 years of employment as a software developer
High School, High School at Sahara International School - IGCSEMaster of Science - MS Computer Engineering, Master of Science - MS Computer Engineering at Cairo University
Arabic, English
Github Skills (5)
deep-reinforcement-learning10
tensorflow10
python10
reinforcement-learning10
opensimulator9
Programming languages (4)
Github contributions (5)
tensorforce/tensorforce
Sep 2018 - Sep 2018
Tensorforce: a TensorFlow library for applied reinforcement learning
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 1 PR in 21 days
Contributions summary:Andrew primarily contributed to the reinforcement learning library by adding new environments and features. They integrated a ViZDoom environment, including the necessary files and an example. Furthermore, they added support for OpenSim environments and made bug fixes. The user's work showcases an ability to extend the library's functionality with diverse reinforcement learning environments.
reinforcement-learningtensorflowdeep-reinforcement-learningtensorflow-librarytensorforce
andrewekhalel/edafa
Oct 2018 - Nov 2021
Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.
Contributions:64 commits, 42 pushes, 1 branch in 3 years 1 month
classificationcomputer-visionsegmentationsuper-resolutionaugmentation
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