People in DSML Kazakhstan

ML Engineer and AI competitor. Member of Russian Team on IOAI 2026, Prize-winner Russian National Olympiad in Informatics 2026, 2025. Open to research collaborations and competitions=)

AI/ML-инженер с годом продакшн-опыта, в основном в computer vision и генеративном AI, плюс LLM/RAG и OCR- пайплайны. Беру системы целиком, от исследовательской статьи или сырого прототипа до развёрнутого сервиса: выбираю и настраиваю модели, собираю пайплайн и держу качество уже в проде. Комфортно работаю в стартап- условиях, когда задача определена не до конца и нужно быстро двигаться и доводить до результата. Работаю на Python с PyTorch и диффузионным стеком, а также с коммерческими API и open-source моделями.

I am an AI Engineer focused on applied LLM systems, NLP, RAG, model adaptation, and AI infrastructure for real-world products. I work on building practical AI assistants, evaluation pipelines, and domain-specific solutions for public sector and enterprise use cases.

Third-year AI&CS student | Computational Biology & Pathology | Bioinformatics & AI. International student in China from Uzbekistan, focused on medical AI research.

Rustem Takhanov is a mathematician at Nazarbayev University whose research spans discrete geometry, combinatorics, optimization, machine learning theory, and theoretical computer science. His recent work focuses on high-dimensional kissing arrangements and sphere packings. In 2026, together with collaborators, he constructed a configuration of 841 spheres kissing a central sphere in 12-dimensional Euclidean space, improving a lower bound that had stood since the work of Leech and Sloane in 1971.

AI/ML engineer who designs and ships LLM-backed systems end to end. Built a production retrieval-augmented memory service for an LLM agent from scratch: hybrid retrieval (BM25 plus vector cosine, fused with Reciprocal Rank Fusion), LLM-driven structured extraction with rule-based fallback, contradiction handling over an evolving knowledge store, and graceful degradation when the model backend is unavailable, all containerized behind a FastAPI service. I care about the parts that make AI systems survive contact with production: latency budgets, failure modes, retrieval quality, and clean service contracts. My foundation is real-time ML systems on real hardware (KAUST MSc, GPA 3.88), where I cut a perception pipeline's per-frame latency from minutes to milliseconds, and multi-model AI orchestration, including a 1st-place hackathon pipeline that gated heavy foundation models and a GPT-4o reasoning layer behind a lightweight filter for cost control. I am looking to apply this systems-and-orchestration mindset to enterprise LLM infrastructure and to grow into an architecture-owning role. Native Qazaq and Russian, professional English, based in Astana.