Boruch Chalk
AI Engineering Principal Engineer at Mobileye
Jerusalem District, Israel
Summary
👤
Senior🎓
Top SchoolBoruch Chalk is an MLOps tech lead with a decade of hands-on experience building and operationalizing AI engineering lifecycles at Mobileye and earlier delivering high-throughput SaaS systems as founder/lead at BagelDB. He is a polyglot full‑stack developer who champions modern engineering practices—TDD, pair programming, and CI/CD—while coaching teams to ship business‑critical software. His work spans cloud-native platforms (GCP/AWS), Go, Python and JavaScript, and includes designing systems that handle thousands of daily API requests with burst capacity and low latency. An active open-source contributor, he has extended the popular Argo Workflows Python integrator Hera to support secrets, artifacts, S3/GCS, and richer task customization—evidence of his focus on making ML orchestration more flexible and production-ready. Based in Jerusalem, he combines systems-level thinking with product-minded delivery, often translating research and complex requirements into pragmatic, testable solutions.
10 years of coding experience
9 years of employment as a software developer
The University of Melbourne
Stackoverflow
23reputation
547reached
0answers
1question
Github Skills (18)
kubernetes10
lib10
python10
workflow-automation10
kubernetes-pods10
argo-workflows10
cloud-native9
docker8
dockers8
aws-s37
gcp7
amazon-s37
s3-bucket7
cordova-plugin6
firebase6
Programming languages (10)
Github contributions (5)
argoproj-labs/hera
Jan 2022 - Feb 2022
Hera makes Python code easy to orchestrate on Argo Workflows through native Python integrations. It lets you construct and submit your Workflows entirely in Python. ⭐️ Remember to star!
Role in this project:
Backend Developer
Backend DeveloperContributions:15 reviews, 8 commits, 11 PRs in 1 month
Contributions summary:Boruch made several contributions focused on enhancing the functionality and flexibility of the Hera library. They implemented optional values for tolerations, added support for mounting secret and config map volumes, and introduced a mechanism to expose task variables as environment variables. Furthermore, the user added support for S3/GCS artifacts and container commands, demonstrating a focus on expanding the capabilities and usability of the Hera library for Argo workflows. They also worked on supporting subclassing of the Task class.
argo-workflowspythonkubernetesmachine-learningworkflow-management
bchalk101/polars
Jan 2024 - Dec 2024
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Contributions:125 pushes, 7 branches in 11 months
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