Full-Stack Developer · ML Systems · Research Engineering
I build business systems, analytical platforms, and ML-driven products with a focus on architecture, reproducibility, evaluation, and engineering clarity.
Software engineer with commercial experience across full-stack development, applied machine learning, and product-oriented system design.
I build business systems, analytical platforms, and ML-driven products with a focus on reproducibility, evaluation, and engineering clarity.
I am particularly interested in software that combines technical rigor with clear real-world value.
Evidence-to-action platform for adaptation prioritization, water resilience, implementation tracking, and climate-finance readiness in Tuvalu.
Designed as a decision-support system at the intersection of geospatial analytics, climate adaptation, and implementation planning.
Production-style sales management system built with Django, DRF, React, and TypeScript.
Focused on client management, order workflows, payments, returns, analytics, and dashboard reporting.
Research-engineered platform for market forecasting and financial analytics.
Combines feature pipelines, experiment workflows, model evaluation, and analytical infrastructure for decision support.
Research-style analytics pipeline focused on the Russian e-commerce market.
Built for statistical analysis, visualization, and structured market interpretation.
Machine learning workflow exploring the relationship between nighttime light intensity and anxiety-related urban patterns.
Combines public data, feature engineering, analytical modeling, and interpretable research framing into a coherent system.
Open-data project and interactive map of Russian border checkpoints.
Combines data normalization, geographic structuring, and public-facing visualization.
I am especially interested in ML and analytical systems that are reproducible, evaluation-driven, and useful in real decision-making contexts.
- Clear architecture over accidental complexity
- Reproducible workflows over manual guesswork
- Evaluation and interpretability over model hype
- Maintainability over short-term shortcuts
- Documentation as part of the product
- Practical value backed by technical rigor
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