{"id":4022672,"date":"2026-09-24T13:49:03","date_gmt":"2026-09-24T11:49:03","guid":{"rendered":"https:\/\/remoteworkukraine.com\/ua\/remote-job\/senior-ml-engineer\/"},"modified":"2026-09-25T23:28:17","modified_gmt":"2026-09-25T21:28:17","slug":"senior-ml-engineer-at-oshchadbank-09-24-2026","status":"publish","type":"job","link":"https:\/\/remoteworkukraine.com\/ua\/remote-job\/senior-ml-engineer-at-oshchadbank-09-24-2026\/","title":{"rendered":"Senior ML Engineer"},"content":{"rendered":"

\u041c\u0406\u0419 \u0411\u0410\u041d\u041a \u041c\u041e\u042f \u041e\u041f\u041e\u0420\u0410.
\u041e\u043f\u043e\u0440\u0430 \u043c\u0456\u043b\u044c\u0439\u043e\u043d\u0456\u0432 \u0443\u043a\u0440\u0430\u0457\u043d\u0446\u0456\u0432, \u0445\u0442\u043e \u0432\u0456\u0434\u0442\u0435\u043f\u0435\u0440 \u043a\u0430\u0436\u0435 \u00ab\u041c\u041e\u0404\u00bb.
\u041c\u041e\u0407 \u041b\u042e\u0414\u0418. \u041c\u041e\u042f \u0421\u041f\u0420\u0410\u0412\u0410. \u041c\u041e\u042f \u0421\u0412\u041e\u0411\u041e\u0414\u0410.<\/p>\n

\u041e\u0429\u0410\u0414\u0411\u0410\u041d\u041a \u0437\u0430\u043f\u0440\u043e\u0448\u0443\u0454 \u0434\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 Senior ML Engineer<\/p>\n

\u041f\u0440\u043e \u0440\u043e\u043b\u044c<\/p>\n

\u0428\u0443\u043a\u0430\u0454\u043c\u043e Senior ML Engineer \u0443 \u043a\u043e\u043c\u0430\u043d\u0434\u0443 \u0437 5 \u043b\u044e\u0434\u0435\u0439 (Team Lead, Middle DS, Senior ML Engineer, Senior Data Engineer, Senior MLOps). \u0426\u0435 hands-on IC \u0440\u043e\u043b\u044c: \u0432\u0438 \u0441\u0430\u043c\u043e\u0441\u0442\u0456\u0439\u043d\u043e \u0434\u0438\u0437\u0430\u0439\u043d\u0438\u0442\u0435 \u0440\u0456\u0448\u0435\u043d\u043d\u044f, \u0440\u0435\u0430\u043b\u0456\u0437\u0443\u0454\u0442\u0435 \u0457\u0445 \u0456 \u0437\u0430\u0445\u0438\u0449\u0430\u0454\u0442\u0435 \u043f\u0435\u0440\u0435\u0434 Team Lead \u0442\u0430 \u0434\u0438\u0440\u0435\u043a\u0442\u043e\u0440\u043e\u043c \u0434\u0435\u043f\u0430\u0440\u0442\u0430\u043c\u0435\u043d\u0442\u0443.
\u041a\u043e\u043c\u0430\u043d\u0434\u0430 \u0432\u0438\u043f\u0443\u0441\u043a\u0430\u0454 batch-\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438 \u0434\u043b\u044f propensity-\u043c\u043e\u0434\u0435\u043b\u0435\u0439 (\u0441\u0445\u0438\u043b\u044c\u043d\u0456\u0441\u0442\u044c \u043a\u043b\u0456\u0454\u043d\u0442\u0430 \u0434\u043e \u043e\u0444\u043e\u0440\u043c\u043b\u0435\u043d\u043d\u044f \u043a\u0440\u0435\u0434\u0438\u0442\u0443, \u043a\u0430\u0440\u0442\u043a\u0438, \u0434\u0435\u043f\u043e\u0437\u0438\u0442\u0443, \u043f\u0440\u0435\u043c\u0456\u0443\u043c-\u043a\u0430\u0440\u0442\u043a\u0438 \u0442\u043e\u0449\u043e). \u0417\u0430\u0440\u0430\u0437 \u0449\u043e\u043c\u0456\u0441\u044f\u0447\u043d\u0438\u0439 \u0446\u0438\u043a\u043b, \u0446\u0456\u043b\u044c \u043f\u0435\u0440\u0435\u0445\u0456\u0434 \u043d\u0430 \u0449\u043e\u0442\u0438\u0436\u043d\u0435\u0432\u0438\u0439. \u0423 production \u043f\u0440\u0430\u0446\u044e\u044e\u0442\u044c \u0430\u043d\u0441\u0430\u043c\u0431\u043b\u0456 \u0437 custom PyTorch \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0442\u0430\u0431\u043b\u0438\u0447\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 \u0442\u0430 tree-based \u043c\u043e\u0434\u0435\u043b\u0435\u0439 (XGBoost, LightGBM, Random Forest). \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0435\u0442\u0430\u043f LLM-\u0437\u0430\u0434\u0430\u0447\u0456: \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0430\u0446\u0456\u044f credit memo \u0442\u0430 \u0432\u043d\u0443\u0442\u0440\u0456\u0448\u043d\u0456\u0439 QA-\u0431\u043e\u0442 \u043f\u043e \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u0457.
\u0406\u043d\u0444\u0440\u0430\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u0433\u0456\u0431\u0440\u0438\u0434\u043d\u0430: \u0434\u0430\u043d\u0456 \u0433\u043e\u0442\u0443\u044e\u0442\u044c\u0441\u044f on-prem (Oracle DB ClickHouse, feature store \u043d\u0430 Feast), \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u0443 \u0445\u043c\u0430\u0440\u0456. \u041e\u0440\u043a\u0435\u0441\u0442\u0440\u0430\u0446\u0456\u044f \u0447\u0435\u0440\u0435\u0437 Airflow, CI\/CD GitLab CI. \u0412\u0430\u043b\u0456\u0434\u0430\u0446\u0456\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 (model risk management) \u043e\u043a\u0440\u0435\u043c\u0430 \u043a\u043e\u043c\u0430\u043d\u0434\u0430, \u0430\u043b\u0435 \u0432\u0438 \u043c\u0430\u0454\u0442\u0435 \u0433\u043e\u0442\u0443\u0432\u0430\u0442\u0438 \u043c\u043e\u0434\u0435\u043b\u0456 \u0442\u0430 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044e \u0434\u043e \u0457\u0445 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u0456\u0432.<\/p>\n

\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0437\u0430\u0434\u0430\u0447\u0456:<\/p>\n

\u041c\u043e\u0434\u0435\u043b\u044e\u0432\u0430\u043d\u043d\u044f (50% \u0447\u0430\u0441\u0443)
\u0420\u043e\u0437\u0440\u043e\u0431\u043a\u0430 \u0442\u0430 \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u044f propensity-\u043c\u043e\u0434\u0435\u043b\u0435\u0439: \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u0430 PyTorch-\u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0442\u0430\u0431\u043b\u0438\u0447\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 (embeddings, custom losses, ensembling strategies), \u0442\u044e\u043d\u0456\u043d\u0433 tree-based \u043c\u043e\u0434\u0435\u043b\u0435\u0439, \u043f\u043e\u0431\u0443\u0434\u043e\u0432\u0430 \u0442\u0430 \u043a\u0430\u043b\u0456\u0431\u0440\u0443\u0432\u0430\u043d\u043d\u044f \u0430\u043d\u0441\u0430\u043c\u0431\u043b\u0456\u0432
\u0413\u0456\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u043d\u0438\u0439 \u043f\u043e\u0448\u0443\u043a (Optuna), \u0432\u0456\u0434\u0442\u0432\u043e\u0440\u044e\u0432\u0430\u043d\u0456 \u0435\u043a\u0441\u043f\u0435\u0440\u0438\u043c\u0435\u043d\u0442\u0438 (MLflow, DVC)
\u041f\u0456\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043e LLM-\u0437\u0430\u0434\u0430\u0447: fine-tuning, RAG-\u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0438, prompt engineering \u0434\u043b\u044f credit memo \u0442\u0430 internal QA
Feature engineering \u0442\u0430 \u0434\u0430\u043d\u0456 (20% \u0447\u0430\u0441\u0443)
\u041f\u0440\u043e\u0435\u043a\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0442\u0430 \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u044f feature pipelines: Oracle DB ClickHouse Feast \u043c\u043e\u0434\u0435\u043b\u044c
\u0420\u043e\u0431\u043e\u0442\u0430 \u0437 \u0432\u0435\u043b\u0438\u043a\u0438\u043c\u0438 \u0442\u0430\u0431\u043b\u0438\u0447\u043d\u0438\u043c\u0438 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430\u043c\u0438: feature selection, temporal feature engineering, \u043e\u0431\u0440\u043e\u0431\u043a\u0430 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0456\u0430\u043b\u044c\u043d\u0438\u0445 \u043e\u0437\u043d\u0430\u043a \u0432\u0438\u0441\u043e\u043a\u043e\u0457 \u043a\u0430\u0440\u0434\u0438\u043d\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u0456
\u0421\u043f\u0456\u0432\u043f\u0440\u0430\u0446\u044f \u0437 Data Engineer \u043d\u0430\u0434 \u044f\u043a\u0456\u0441\u0442\u044e \u0442\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0456\u0441\u0442\u044e \u0434\u0430\u043d\u0438\u0445
Production readiness (20% \u0447\u0430\u0441\u0443)
\u0414\u043e\u043a\u0435\u0440\u0438\u0437\u0430\u0446\u0456\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439, \u043f\u0456\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 inference-\u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0456\u0432 \u0434\u043b\u044f batch-\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0456\u0432
\u041e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u044f throughput \u0434\u043b\u044f \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u0443 \u0437 \u043c\u0456\u0441\u044f\u0447\u043d\u043e\u0433\u043e \u043d\u0430 \u0442\u0438\u0436\u043d\u0435\u0432\u0438\u0439 \u0446\u0438\u043a\u043b (profiling, \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u044f I\/O, \u043f\u0430\u0440\u0430\u043b\u0435\u043b\u0456\u0437\u0430\u0446\u0456\u044f)
\u0406\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0456\u044f \u0437 Airflow DAGs \u0434\u043b\u044f scheduling \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u044c \u0442\u0430 inference
\u041f\u0456\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0430\u0440\u0442\u0435\u0444\u0430\u043a\u0442\u0456\u0432 \u0434\u043b\u044f model validation \u043a\u043e\u043c\u0430\u043d\u0434\u0438: \u043c\u0435\u0442\u0440\u0438\u043a\u0438, backtesting, \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044f \u0441\u0442\u0430\u0431\u0456\u043b\u044c\u043d\u043e\u0441\u0442\u0456
\u041f\u0440\u043e\u0446\u0435\u0441\u0438 \u0442\u0430 \u043a\u043e\u043c\u0443\u043d\u0456\u043a\u0430\u0446\u0456\u044f (10% \u0447\u0430\u0441\u0443)
\u0417\u0430\u0445\u0438\u0441\u0442 \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0445 \u0440\u0456\u0448\u0435\u043d\u044c \u043f\u0435\u0440\u0435\u0434 Team Lead \u0442\u0430 \u0434\u0438\u0440\u0435\u043a\u0442\u043e\u0440\u043e\u043c \u0434\u0435\u043f\u0430\u0440\u0442\u0430\u043c\u0435\u043d\u0442\u0443
Code review, \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0430 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044f
\u041f\u043e\u044f\u0441\u043d\u0435\u043d\u043d\u044f \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0456\u0432 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0431\u0456\u0437\u043d\u0435\u0441- \u0442\u0430 \u0440\u0438\u0437\u0438\u043a-\u0441\u0442\u0435\u0439\u043a\u0445\u043e\u043b\u0434\u0435\u0440\u0430\u043c (feature importance, SHAP)<\/p>\n

\u041e\u0431\u043e\u0432'\u044f\u0437\u043a\u043e\u0432\u0456 \u043d\u0430\u0432\u0438\u0447\u043a\u0438:<\/p>\n

ML \/ DL \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u0438\u0439 \u0440\u0456\u0432\u0435\u043d\u044c
4+ \u0440\u043e\u043a\u0438 \u0434\u043e\u0441\u0432\u0456\u0434\u0443 \u0432 ML, \u0437 \u043d\u0438\u0445 2+ \u0440\u043e\u043a\u0438 \u0437 \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438 \u0432 production
PyTorch: custom \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u0438 \u0434\u043b\u044f \u0442\u0430\u0431\u043b\u0438\u0447\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 (embeddings \u0434\u043b\u044f \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0456\u0430\u043b\u044c\u043d\u0438\u0445 \u0444\u0456\u0447\u0435\u0439, numerical encodings, custom loss functions)
XGBoost \/ LightGBM \/ CatBoost: \u0434\u043e\u0441\u0432\u0456\u0434 \u0442\u044e\u043d\u0456\u043d\u0433\u0443 \u0442\u0430 production-\u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f
Ensemble methods: stacking, blending, calibration \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0456\u0457
Feature engineering \u0434\u043b\u044f high-dimensional \u0442\u0430\u0431\u043b\u0438\u0447\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445, \u0440\u043e\u0431\u043e\u0442\u0430 \u0437 temporal features
Hyperparameter optimization (Optuna)
Explainability: SHAP, feature importance \u0434\u043b\u044f \u043a\u043e\u043c\u0443\u043d\u0456\u043a\u0430\u0446\u0456\u0457 \u0437 \u0431\u0456\u0437\u043d\u0435\u0441\u043e\u043c \u0442\u0430 model validation
LLM \/ NLP \u0431\u0430\u0437\u043e\u0432\u0438\u0439+ \u0440\u0456\u0432\u0435\u043d\u044c
\u041f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u0438\u0439 \u0434\u043e\u0441\u0432\u0456\u0434 \u0437 LLM: fine-tuning \u0430\u0431\u043e RAG-\u043f\u0430\u0439\u043f\u043b\u0430\u0439\u043d\u0438
\u0420\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440 (Transformer, attention), prompt engineering
\u0414\u043e\u0441\u0432\u0456\u0434 \u0437 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430\u043c\u0438: LangChain \/ LlamaIndex \/ vLLM \u0430\u0431\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438
\u0414\u0430\u043d\u0456 \u0442\u0430 \u0456\u043d\u0444\u0440\u0430\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430
SQL \u043d\u0430 \u0440\u0456\u0432\u043d\u0456 \u0441\u043a\u043b\u0430\u0434\u043d\u0438\u0445 \u0430\u043d\u0430\u043b\u0456\u0442\u0438\u0447\u043d\u0438\u0445 \u0437\u0430\u043f\u0438\u0442\u0456\u0432 (Oracle, PostgreSQL \u0430\u0431\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438)
\u0414\u043e\u0441\u0432\u0456\u0434 \u0437 \u043a\u043e\u043b\u043e\u043d\u043a\u043e\u0432\u0438\u043c\u0438 \u0421\u0423\u0411\u0414 (ClickHouse \u0430\u0431\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438)
\u0420\u043e\u0437\u0443\u043c\u0456\u043d\u043d\u044f feature store \u043a\u043e\u043d\u0446\u0435\u043f\u0446\u0456\u0457 (Feast \u043f\u043b\u044e\u0441)
Docker: \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0442\u0430 \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u044f \u043e\u0431\u0440\u0430\u0437\u0456\u0432 \u0434\u043b\u044f ML-\u0441\u0435\u0440\u0432\u0456\u0441\u0456\u0432
Software Engineering
Python 3.10+ (strong): \u0442\u0438\u043f\u0456\u0437\u0430\u0446\u0456\u044f, \u043c\u043e\u0434\u0443\u043b\u044c\u043d\u0430 \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u0430, \u0447\u0438\u0441\u0442\u0438\u0439 \u043a\u043e\u0434
Git, code review, \u0442\u0435\u0441\u0442\u0438 (pytest)
\u0414\u043e\u0441\u0432\u0456\u0434 \u0440\u043e\u0431\u043e\u0442\u0438 \u0437 Airflow \u0430\u0431\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0456\u0447\u043d\u0438\u043c \u043e\u0440\u043a\u0435\u0441\u0442\u0440\u0430\u0442\u043e\u0440\u043e\u043c
Experiment tracking \u0442\u0430 model versioning (MLflow, DVC, W&B)<\/p>\n

\u0411\u0443\u0434\u0435 \u043f\u043b\u044e\u0441\u043e\u043c:
\u0414\u043e\u0441\u0432\u0456\u0434 \u0443 \u0431\u0430\u043d\u043a\u0456\u0432\u0441\u044c\u043a\u0456\u0439 \u0441\u0444\u0435\u0440\u0456 \u0430\u0431\u043e \u0444\u0456\u043d\u0430\u043d\u0441\u043e\u0432\u0438\u0445 \u0441\u0435\u0440\u0432\u0456\u0441\u0430\u0445 (propensity, credit scoring, churn)
ONNX \/ TorchScript \u0434\u043b\u044f \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u0430\u0446\u0456\u0457 inference
Distributed training (PyTorch DDP)
\u0414\u043e\u0441\u0432\u0456\u0434 \u0437 drift detection \u0442\u0430 automated retraining pipelines
\u0420\u043e\u0431\u043e\u0442\u0430 \u0437 PII: \u0430\u043d\u043e\u043d\u0456\u043c\u0456\u0437\u0430\u0446\u0456\u044f, \u043c\u0430\u0441\u043a\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u0430\u043d\u0438\u0445<\/p>\n

\u041c\u0438 \u0433\u0430\u0440\u0430\u043d\u0442\u0443\u0454\u043c\u043e:
\u0443\u043c\u043e\u0432\u0438 \u0434\u043b\u044f \u043f\u0440\u043e\u0444\u0435\u0441\u0456\u0439\u043d\u043e\u0433\u043e \u0442\u0430 \u043a\u0430\u0440'\u0454\u0440\u043d\u043e\u0433\u043e \u0437\u0440\u043e\u0441\u0442\u0430\u043d\u043d\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u043f\u0456\u0432\u0440\u043e\u0431\u0456\u0442\u043d\u0438\u043a\u0430;
\u043e\u0444\u0456\u0446\u0456\u0439\u043d\u0435 \u043f\u0440\u0430\u0446\u0435\u0432\u043b\u0430\u0448\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0456\u0437 \u0434\u043e\u0442\u0440\u0438\u043c\u0430\u043d\u043d\u044f\u043c \u0432\u0441\u0456\u0445 \u0441\u043e\u0446\u0456\u0430\u043b\u044c\u043d\u0438\u0445 \u0433\u0430\u0440\u0430\u043d\u0442\u0456\u0439 \u0437\u0433\u0456\u0434\u043d\u043e \u041a\u0417\u043f\u041f (\u043e\u043f\u043b\u0430\u0447\u0443\u0432\u0430\u043d\u0456 \u043b\u0456\u043a\u0430\u0440\u043d\u044f\u043d\u0456, \u0432\u0456\u0434\u043f\u0443\u0441\u0442\u043a\u0443 \u0432\u0456\u0434 29 \u043a\u0430\u043b\u0435\u043d\u0434\u0430\u0440\u043d\u0438\u0445 \u0434\u043d\u0456\u0432 \u043d\u0430 \u0440\u0456\u043a);
\u0431\u0435\u0437\u043a\u043e\u0448\u0442\u043e\u0432\u043d\u0435 \u043c\u0435\u0434\u0438\u0447\u043d\u0435 \u0441\u0442\u0440\u0430\u0445\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u043b\u044f \u0432\u0441\u0456\u0445 \u043f\u0440\u0430\u0446\u0456\u0432\u043d\u0438\u043a\u0456\u0432.
\u041d\u0430\u0434\u0441\u0438\u043b\u0430\u0439\u0442\u0435 \u0412\u0430\u0448\u0435 \u0440\u0435\u0437\u044e\u043c\u0435 \u0441\u0442\u0430\u043d\u044c\u0442\u0435 \u0447\u0430\u0441\u0442\u0438\u043d\u043e\u044e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 \u041e\u0429\u0410\u0414\u0423!<\/p>\n","protected":false},"excerpt":{"rendered":"

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