Developing Backup Modules and Automating Infrastructure

Developed over 15 backup modules in Python for systems like PostgreSQL, ZFS, SharePoint, MS SQL, and VMware vCenter.

Standardised module architecture and config format (YAML), improving team productivity and onboarding.

Built the alpha version of the web UI and REST API, laying the foundation for further product evolution.

Deployed corporate GitLab and configured CI/CD pipelines for automated testing and deployment.

Created test stands using Proxmox and wrote Go-based CLI tools for the QA department.

Over a three-year period, we worked closely with one of Russia's largest banks to successfully establish a robust AI monitoring system. This system operates with precision to swiftly address system failures by pinpointing their root causes and making well-informed decisions. The model expanded the scope of forecasted indicators to a total of 50,000, achieving an 85% reduction in false positive alerts. It also delivered a 20% improvement in precision compared to previous methodologies.

Led a team to develop an AI monitoring system that was capable of detecting anomalies in various business indicators. By closely working with one of Russia's largest banks over a span of 3 years, we designed and implemented an advanced backend system dedicated to the detection of anomalies across a wide spectrum of critical business metrics. This system significantly decreased the time and effort previously invested in manual incident detection and enhanced operational efficiency.

I coordinated a team in the development of two distinct automated incident detection models, which yielded substantial improvements in incident detection times, reducing them by an impressive 5 to 15 minutes. These models played a pivotal role in streamlining processes and optimizing the bank's operations.

• Designed 5+ comprehensive Power BI dashboards for operational performance across departments, with 30+ daily users.
• Built core data marts in PostgreSQL focused on the "order" entity, forming the foundation for company-wide reporting.
• Automated daily, weekly, and monthly data exports for the operations team using Python scripts.
• Conducted regular ad-hoc analysis to support ongoing business needs and decision-making.