Alaeddine Abdessalem
Machine Learning Engineer at Deutsche Telekom
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolAlaeddine Abdessalem is a Machine Learning Engineer based in Berlin with six years of experience building and deploying scalable ML and backend systems. Currently at Deutsche Telekom, he brings practical expertise in LLM fine-tuning and productionization from prior engineering roles at Jina AI and other startups. He has strong backend and DevOps chops—contributing to notable open-source projects like Jina Serve and DocArray to improve offline execution, storage backends, and robustness for multimodal AI workloads. Comfortable across CI/CD, AWS deployments and storage optimizations, he blends research-oriented ML work with production-first engineering. Colleagues appreciate his knack for turning tricky integration and compatibility issues into reliable, test-covered solutions.
6 years of coding experience
4 years of employment as a software developer
Diplôme d'ingénieur, Software Engineering, Diplôme d'ingénieur, Software Engineering at National Institute of Applied Science and Technology
Stackoverflow
26reputation
55reached
1answer
0questions
Github Skills (30)
weaviate10
docker10
sqlite10
back-end-development10
python10
dockers10
backend10
testing9
amazon-elasticsearch9
data-structure9
qdrant9
aws-elasticsearch9
deeplearning-ai9
elasticsearchquery9
deep-learning9
Programming languages (8)
Github contributions (5)
docarray/docarray
Feb 2022 - Jan 2023
Represent, send, store and search multimodal data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:376 reviews, 135 commits, 133 PRs in 11 months
Contributions summary:Alaeddine primarily contributed to the back-end functionality of the `docarray/docarray` repository, focusing on storage backends and related features. Their work included making SQLite document arrays pickable, fixing Weaviate integration issues, and implementing batch operations for efficiency. Furthermore, they improved storage summaries, implemented a list-based offset2id, and refactored various array operations. The contributions enhanced storage performance and expanded the capabilities of the document array library.
protobuffeature-storevectorunstructured-dataqdrant
jina-ai/serve
Jul 2021 - Jan 2023
☁️ Build multimodal AI applications with cloud-native stack
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:445 reviews, 229 commits, 265 PRs in 1 year 6 months
Contributions summary:Alaeddine's commits focus on improving the Jina AI serve repository. They addressed Docker version compatibility issues by raising exceptions for older versions, enhancing the system's robustness. Additionally, they supported offline executor capabilities, indicating a focus on making the system work even without an internet connection. The commits also indicate improvements to testing, with tests that target the robustness of the software.
opentelemetryllm-inferencecreative-aigrpcproduction
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