Eran Trabelci

AI Engineer at Nebius Academy Israel

Jerusalem, Jerusalem District, Israel
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Summary

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Senior
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Eran Trabelci is an AI Engineer based in Tel Aviv with eight years of experience turning geospatial, operational and scientific problems into production-ready data and ML solutions. He built map infrastructure and routing configurations from scratch at Via, progressed to senior roles defining training frameworks and internal Python tooling, and now focuses on AI engineering at Binatna. His background combines an M.Sc. in Geomorphology and a strong mathematics foundation with recent honors-level data science training, enabling him to bridge domain science and scalable software. Known for improving team workflows (for example restructuring git repos) and for pragmatic root-cause analysis of operational issues, he excels at making complex algorithms usable in production. He brings a rare mix of field-domain expertise, systems engineering and hands-on model/data tooling that helps teams deploy robust, data-driven products.
code8 years of coding experience
job4 years of employment as a software developer
bookMaster of Science (M.Sc.) Geology/Earth Science, Geomorphology, Master of Science (M.Sc.) Geology/Earth Science, Geomorphology at The Hebrew University of Jerusalem
bookData science, Data science at Nebius Academy Israel
bookCertificate Computer Science, Certificate Computer Science at The Open University of Israel
languagesFrench, English
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Github Skills (27)

python10
semantic-search10
retrieval-augmented-generation10
ai10
natural-language-processing10
nlp10
llama-index10
langchain9
evaluation9
monitoring9
ycombinator9
llm9
large-language-models9
machine-learning9
vector-database9

Programming languages (4)

TypeScriptCJupyter NotebookPython

Github contributions (5)

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OrChen17/assembler_project

Jun 2022 - Aug 2022

Contributions:8 reviews, 3 PRs, 76 pushes in 2 months
erantr1/RAG_Techniques

Jan 2025 - Apr 2025

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Contributions:18 pushes, 2 branches in 3 months
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