Gal Topper
Israel
Summary
🤩
Rockstar🎓
Top SchoolGal Topper is a seasoned software engineer with 11 years of experience building scalable, data-heavy systems and MLOps integrations. He has deep expertise in asynchronous, nonblocking architectures—working with Scala/Akka, Golang, Spark and Kafka—to deliver high-throughput services and feature-store pipelines. At Iguazio he combined low-level data access with client-side Python APIs and cloud-native runtimes, and his open-source contributions to MLRun improved Kafka/Snowflake integrations and Spark-based ingest for production ML pipelines. Previously he led backend engineering for an algorithmic ad server and introduced robust streaming, monitoring, and data-store patterns across diverse datastores. Based in Israel, he favors functional approaches and simplicity, turning complex distributed problems into pragmatic, maintainable systems. He’s equally comfortable diving into C-level performance fixes or shaping high-level MLOps workflows, a blend that shows in both product-facing features and plumbing-level reliability.
11 years of coding experience
13 years of employment as a software developer
Bachelor’s Degree, Computer Science, B+, Bachelor’s Degree, Computer Science, B+ at York University
Hebrew, English, German, Hungarian
Github Skills (12)
machine-learning10
feature-store10
mlops10
python10
kubernetes-pods9
data-engineering9
spark9
workflow-engine9
kubernetes9
kafka8
experiment8
data-science8
Programming languages (6)
Github contributions (5)
mlrun/mlrun
Jun 2022 - Jan 2023
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:1220 reviews, 76 commits, 806 PRs in 7 months
Contributions summary:Gal contributed primarily to the MLRun MLOps platform, focusing on improving data source integrations and feature store functionalities. Their commits involved fixing issues within the data store, such as Kafka and Snowflake integrations, and resolving bugs related to the feature store's ingest process with Spark. Additionally, the user implemented improvements in model monitoring, fixing TSDB target usage. The user demonstrated a strong understanding of the platform's components, including datastore, feature store, and serving runtimes.
experiment-trackingpythondata-sciencecmlmachine-learning
v3io/v3io-fs
Jun 2021 - Nov 2022
v3io fsspec (filesystem_spec) drivers
Contributions:11 releases, 22 reviews, 22 commits in 1 year 4 months
driversfsmime-typesvfsconnector
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