Hjalti Sigurbjörnsson
System Administrator
Iceland
Summary
👤
Senior🎓
Top SchoolHjalti Sigurbjörnsson is a systems-focused engineer with a decade of experience spanning back-end development, Linux administration, and infrastructure at Íslandsbanki. He primarily codes in Go and Python, contributing performance and usability improvements to the open-source Feast feature store used in ML pipelines, and brings practical DevOps sensibilities to production systems. At Íslandsbanki he has worked across API management, IAM, networking and automation, and currently manages RHEL/Oracle Linux environments with Python scripting. Comfortable bridging software and operations, he also has hands-on experience with SCCM, Active Directory and PowerShell from earlier roles. Hjalti’s background in both commercial web/API projects and open-source ML tooling gives him a pragmatic edge in optimizing data paths and deployment workflows.
10 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Reykjavik UniversityStudent, Business/Commerce, General, Student, Business/Commerce, General at Commercial College of Iceland
Github Skills (17)
feature-store10
postgresql10
python10
data-engineering10
performance-optimization10
fastapi9
mlops9
rest-api8
api-rest8
restful-api8
data-science8
api-design8
dockers7
big-data7
docker7
Programming languages (5)
Github contributions (5)
feast-dev/feast
Oct 2021 - Apr 2022
The Open Source Feature Store for Machine Learning
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:4 reviews, 7 commits, 6 PRs in 5 months
Contributions summary:Hjalti primarily contributed to improving the performance and functionality of the Feast feature store. They optimized code for faster data processing within the `_convert_arrow_to_proto` function by using dictionaries for faster lookups. Additionally, the user implemented features such as printing the entire registry as JSON and added a host option for the serve command, enhancing the tool's debugging and deployment capabilities. The user also addressed issues around data consistency, specifically around how `FeatureStore.apply` interacts with the registry and infrastructure updates.
pythondata-qualitydata-sciencemlmachine-learning
nossrannug/feast-postgres
Oct 2021 - Apr 2022
PostgreSQL offline and online stores for Feast
Contributions:29 commits, 3 PRs, 25 pushes in 6 months
postgresqlofflinestoresfeast
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