Aleksandr Golubovskij is a seasoned full-stack engineer and prompt engineer with a decade of experience building high-availability, high-load services and AI-driven products from Lithuania. As a tech lead he has delivered fintech and telecom systems, Kubernetes-powered backends, and ML-infused features like face detection for fraud prevention, while currently shaping an AI assistant for public procurement at TEDAS.LT. He contributes to open-source tooling—extending Elasticsearch drivers with geo-spatial filters and robust tests—demonstrating a knack for pragmatic backend enhancements and QA. Known as a troubleshooter who improves development processes and cross-team testing (Cypress), he blends hands-on coding with team leadership and operational responsibility. Practical, detail-oriented, and curious, he pairs infrastructure sensibility (APN/static IP, K8s) with product-focused engineering to turn complex requirements into reliable systems.
10 years of coding experience
6 years of employment as a software developer
Information Technology, Information Technology at Vilniaus universitetas / Vilnius University
This package offers advanced functionality for searching and filtering data in Elasticsearch.
Role in this project:
Back-end Developer & QA Engineer
Contributions:6 commits, 1 PR, 5 comments in 13 days
Contributions summary:Aleksandr focused on extending the functionality of the Elasticsearch driver by adding geo-spatial filters, including `geo_distance`, `geo_bounding_box`, and `geo_polygon`. They implemented these features in the `FilterBuilder` class. The user also wrote and fixed tests for the newly added functionality.
Contributions:2 releases, 23 commits, 18 pushes in 18 days
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