Yaron Haviv
Tech Leader at McKinsey & Company
Netanya, Center District, Israel
Summary
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Rockstar🎓
Top SchoolYaron Haviv is a seasoned tech leader and founder with over two decades of experience building high-performance networking, storage, and AI/ML platforms; he co-founded and served as CTO of Iguazio and now leads technology initiatives within McKinsey’s QuantumBlack. He has a strong track record of taking data-intensive systems from research to production—designing unified real-time database engines, serverless ML pipelines, and scalable model-serving infrastructures that power enterprise and cloud partners worldwide. Earlier executive roles at Mellanox and Voltaire reflect deep expertise in datacenter architecture, protocols, and solution integration across hardware and software boundaries. Active in the open-source MLOps space, he has contributed documentation and serving improvements to MLRun, demonstrating a hands-on approach to developer experience as well as system design. Based in Netanya, Israel, he blends founder-level product vision with large-scale engineering leadership to accelerate AI proofs-of-concept into reliable, production-grade platforms.
9 years of coding experience
9 years of employment as a software developer
BSc Computer and Electrical engineering, BSc Computer and Electrical engineering at Tel Aviv University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (6)
mlops10
python10
documentation10
workflow-engine9
kubernetes8
kubernetes-pods8
Programming languages (11)
Github contributions (5)
mlrun/mlrun
Jun 2022 - Dec 2022
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:13 releases, 496 reviews, 35 commits in 6 months
Contributions summary:Yaron primarily focused on updating and refining documentation and tutorials for the MLRun platform. They updated broken links, added content, and restructured existing documentation to improve clarity and user experience. Additionally, the user made several commits related to the serving component, including adding a parallel run router and enhancing the mock server functionality.
experiment-trackingpythondata-sciencecmlmachine-learning
mlrun/demo-xgb-project
Jan 2020 - Mar 2020
demo the work of MLRun with Github webhooks
Contributions:1 release, 50 commits, 4 PRs in 2 months
webhooks
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