Eyal Trabelsi is a Software Architect with over a decade of experience designing and operating petabyte-scale, low-latency data and ML systems from Tel-Aviv. He has led engineering organizations of 50+ engineers to serve millions of requests per second, migrate real-time databases to Aerospike, and consolidate analytics into a single source of truth using Firebolt while driving SOC 2 and GDPR programs. Hands-on across the stack, Eyal specializes in Python, performance optimizations, Kubernetes-based microservices, distributed training, ONNX serving, and scalable ETL built on Spark and Kafka. He pairs technical leadership with practitioner-level execution—streamlining ML lifecycle, CI/CD, experiment tracking, and backfill processes to cut issue resolution from months to days. An active open-source contributor and technical writer, he has improved user onboarding for the Numba project and publishes about engineering and ML best practices. Colleagues know him for candid executive feedback and a focus on measurable operational improvements rather than ivory-tower designs.
9 years of coding experience
11 years of employment as a software developer
Bachelor of Science (BSc) Computer Science, Bachelor of Science (BSc) Computer Science at Ben-Gurion University of the Negev
Master's degree with thesis Computer Science, Master's degree with thesis Computer Science at The Open University of Israel
Contributions:8 commits, 1 PR, 4 comments in 11 days
Contributions summary:Eyal's commits primarily involve updates to the documentation files within the `docs/source/user/faq.rst` directory. These updates focus on clarifying installation instructions for development builds, correcting typos, and revising formatting for readability. The user edited and revised the content regarding how to install pre-release versions of Numba using conda commands, ensuring accuracy and clarity in the documentation for users. The changes reflect a focus on user onboarding and providing accessible information regarding development builds.
The goal of pandas-log is to provide feedback about basic pandas operations. It provides simple wrapper functions for the most common functions that add additional logs
Contributions:22 commits, 12 PRs, 81 pushes in 1 year 3 months
pythongoalwrapper-functionsadditionalfeedback
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