● Contributed to the development of an internal service for automating the diagnosis of production \& performance issues by creating diagnostic sessions.● Managed product decisions, implementing backend optimizations and UI enhancements for a more intuitive, high-performance user experience.● Streamlined the generation of specialized profiling diagnostic sessions and flamegraph creation through new architectural solutions and a notification system that informs users of session statuses via Slack and Jira integration.● Conducted prompt engineering research to support Cortex AI (LLM) integration, automating optimal parameter selection for diagnostic sessions.● Designed a diagnostic session summary mechanism, leveraging LLM integration and prompt engineering to condense extensive results into actionable insights, reducing developers' analysis time.

● Configured an effective operation of the IntelliJ TestSpark plugin -- a plugin for generating unit tests that natively integrates different AI-based tools in the IDE.● Revamped the plugin architecture, enabling easy addition of new languages beyond the initial monolithic setup.● Added Kotlin language support to the plugin.

● Researched in the field of Content Defined Chunking algorithms for the acceleration of the download time of a set of akin files.● Implemented a framework for testing different chunking approaches on the Snowflake-specific tar archives.● Conducted an exploration of various techniques based on the key performance metrics.● Performed the full-stack flow of integrating the new approach to downloading tar archives into the Snowflake backend.✔ As a result, the downloading speed of chunked archives was decreased by 30 percent, underscoring a reduction of the company’s expenses.● Accomplished algorithm is now an important part of future breakthrough optimization of the archive downloading flow.● Migrated alerts and dashboards of my team from Wavefront to Grafana (PromQL).

The work was done under the scope of a bachelor's thesis.● Researched in a field of regression tests runtime acceleration: Test Case Prioritization, Test Case Selection, and Test Case Minimization.● Designed the framework for applying advanced Test Prioritization techniques to diverse projects, which took into account project-specific intricacies and the nuances of their testing pipelines.● Leveraged the framework to identify the most effective, custom-tailored solution to the IntelliJ IDEA testing flow, relying on a large number of carefully curated metrics.● Created the seamless integration of the selected algorithm into the real CI/CD environment of IntelliJ IDEA.✔ As a result, the time until the first error detection was accelerated by 300 times for some builds.