Диас Болатов

Artificial Intelligence Engineer (Intern)

Developed risk-classification algorithms and ML models in Python (scikit-learn, CatBoost, LightGBM), applying cross-validation and class-balancing techniques. Built end-to-end ETL pipelines for data collection, feature computation and model training, with experiment and data versioning (DVC, MLflow). Integrated a retrieval-augmented generation (RAG) stack using embeddings with vector and hybrid search. Deployed and maintained models via APIs (FastAPI, REST, Docker). Delivered an ML system forecasting risk in shared-equity construction projects for the citizens’ rights-protection portal. Built a RAG-based AI assistant (bge-m3, FAISS, hybrid search, reranking) producing answers grounded in the regulatory knowledge base. Automated the full workflow - data collection and cleaning from government registries, model training and scoring - and implemented delivery-timeline forecasting from readiness time-series with delay-probability estimation.

• Delivered an ML system forecasting risk in shared-equity construction projects for the citizens’ rights-protection portal.
• Built a RAG-based AI assistant (bge-m3, FAISS, hybrid search, reranking) producing answers grounded in the regulatory knowledge base.
• Automated the full workflow - data collection and cleaning from government registries, model training and scoring - and implemented delivery-timeline forecasting from readiness time-series with delay-probability estimation.