People in DSML Kazakhstan

AI Research Engineer with ~4 years in ML/AI, focused on agentic systems that hold up in production. At Raiffeisenbank, I architected a multi-agent AI platform from scratch: autonomous agents generated product hypotheses, and 67% of them were accepted by product teams for testing. Earlier, I built an A/B testing framework whose experiments delivered a $25k monthly revenue uplift. Today I work on both sides of the field. At AIRI, I develop agentic VLM-based RAG for document understanding, currently outperforming published SOTA baselines. At HSE, I mentor graduate students in Advanced NLP, from Transformer internals to multi-agent systems. Before AI, I built my career in aviation rising from Maintenance Engineer to Production Manager of 60+ engineers in safety-critical operations. That is where my production-first mindset comes from: an AI system is only as good as its behavior under real-world constraints. Technical Stack: • Agentic & LLM: Agentic Workflows, LangChain, LangGraph, MCP, RAG, vLLM, Transformers • ML & Evaluation: Python, PyTorch, scikit-learn, Ragas, Experimental Design, A/B Testing, Drift Monitoring • MLOps & Orchestration: Apache Airflow, Kubernetes, Docker, CI/CD, MLflow • Data & Databases: PySpark, SQL, Hadoop, Vector DBs (Milvus), MongoDB, AWS S3 • Backend & Tools: FastAPI, Litestar, Kafka, RabbitMQ, Redis, Bash, Linux, Git, Pytest Open to roles: AI Research Engineer • LLM Engineer • Agentic AI Engineer • Applied Research Scientist

Data Scientist с 1 годом коммерческого опыта разработки продакшен ML-моделей и аналитических решений. Основной опыт — табличный ML, прогнозирование и обработка больших объёмов данных в PySpark; также есть практический опыт в NLP, deep learning и computer vision.

I’m an ML Engineer working across LLM/VLM systems, computer vision, and 3D reconstruction. I’m especially interested in applied ML research, model evaluation and optimization, and turning experimental ideas into reliable production systems.