Mohamed Belaweid
AI Engineer at Aleph Alpha
Berlin, Germany
Summary
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Rockstar🎓
Top SchoolMohamed Belaweid is an AI engineer based in Berlin with six years of experience building and productionizing machine learning systems, especially around LLMs, multimodal search, and GPU-accelerated training. He has shipped retrieval and embedding pipelines at Jina AI, improved search for media at scale, and worked on legal-domain language models and NER workflows with RelationalAI, demonstrating strong end-to-end ML product experience from data collection to deployment. At SoundCloud and Aleph Alpha he continued to focus on scalable ML infrastructure and model-driven features, while his open-source QA work on docarray shows attention to numerical robustness across frameworks like NumPy, PyTorch, TensorFlow and Paddle. Known for optimizing and automating workflows, he combines practical engineering (model serving, TensorRT, BentoML) with a knack for improving test coverage and reproducibility in ML projects.
6 years of coding experience
4 years of employment as a software developer
Software engineer Software engineering, Software engineer Software engineering at INSAT - Institut National des Sciences Appliquées et de Technologie
English, French, Arabic, German
Github Skills (11)
unit-testing10
torch-hub10
paddle10
torch-net10
tensorflow10
pytest10
python10
numpy10
data-structure9
data-structures9
machine-learning8
Programming languages (4)
Github contributions (5)
docarray/docarray
Mar 2022 - Apr 2022
Represent, send, store and search multimodal data
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:2 reviews, 7 commits, 17 PRs in 1 month
Contributions summary:Mohamed primarily focused on writing and improving unit tests within the `docarray/docarray` repository. Their contributions involved creating tests for distance calculations using NumPy, TensorFlow, Paddle and Torch, including sparse matrix implementations. They also added more comprehensive test coverage, ensuring the robustness and reliability of the project's core mathematical functionalities. Furthermore, the user enhanced test tolerance by adjusting the precision of assertion to improve accuracy.
protobuffeature-storevectorunstructured-dataqdrant
jina-ai/executor-qdrant-indexer
Apr 2022 - Sep 2022
Contributions:1 release, 22 commits, 2 PRs in 5 months
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