Omar Shouman
Cloud And AI Lead at Robusta Technology Group (RTG)
Germany
Summary
👤
Senior🎓
Top SchoolOmar Shouman is a Cloud and AI Lead with a decade of experience building and operationalizing machine learning solutions across academia and industry, most recently leading Cloud & AI at Robusta Technology Group after an AI/ML Data Scientist role at AWS. He combines a Dr. rer. nat. in applied ML for proteomics and an MS in Informatics with hands-on MLOps expertise on AWS to deliver scalable, reliable ML systems that align with business objectives. His background spans research positions at TUM, SAP UCC and LMU to product-focused engineering roles, giving him a rare fluency in both cutting-edge ML research and production-grade deployment. Beyond engineering, Omar has led teams, driven performance improvement and talent development, and taught master-level ML courses—bridging technical depth with people leadership. Based in Germany, he brings interdisciplinary perspective from life-sciences applications to cloud-native AI, often translating complex research into practical, measurable outcomes.
10 years of coding experience
12 years of employment as a software developer
Bachelor of Science - BS, Information Engineering and Technology, 1.45 Excellent with Honors (equivalent GPA 3.5/4.0), Bachelor of Science - BS, Information Engineering and Technology, 1.45 Excellent with Honors (equivalent GPA 3.5/4.0) at The German University in CairoDr. rer. nat., Applied Machine Learning in Proteomics, Dr. rer. nat., Applied Machine Learning in Proteomics at Technical University of Munich
English, German, Arabic
Github Skills (8)
mass-spectrometry10
spectrum10
machine-learning9
deep-learning8
annotations8
python8
refactoring5
generative-ai1
Programming languages (3)
Github contributions (5)
wilhelm-lab/dlomix
Jun 2021 - Nov 2022
Python framework for Deep Learning in Proteomics
Contributions:15 releases, 8 reviews, 105 commits in 1 year 6 months
deep-learningpythonproteomicspython-frameworkframework
wilhelm-lab/PROSPECT
May 2022 - Mar 2023
Proteomics Mass Spectrometry Datasets for Machine Learning
Contributions:1 release, 41 commits, 15 PRs in 9 months
annotationsdatasetdeep-learningfragment-ionsmachine-learning
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