All people
Browse public community profiles by work, location, or collaboration interests.
Danial Albertuly
Connected with DSML. This person's Telegram account has been confirmed through DSML Kazakhstan.@daniastkz
16 y.o Student Founder from Astana, Kazakhstan. Interested in AI / ML Research. Open to Work
Nooruzbek Toktobaev
@NooruzbekT
International Information Technologies University
Software engineering student with hands-on ML and NLP experience. Worked with transformer models, embeddings, and distillation in production at a real bank. Currently building LLM-powered agents and RAG pipelines. Interested in expanding into generative and multimodal AI research.
Agzam Shamsadinov
Connected with DSML. This person's Telegram account has been confirmed through DSML Kazakhstan.@Mazga
student · NSPM
Интересуюсь машинным обучением, глубоким обучением, искусственным интеллектом, анализом данных, программированием и вообще думаю это прикольно
Nurdaulet Serketay
@Nurda
Data Analyst Inern · Jana Post
Final-year Information Systems student at SDU University focused on data analytics, SQL, Python, and business intelligence. Interested in transforming raw data into clear insights, dashboards, and practical recommendations for business decision-making. Looking for a Junior Data Analyst or Data Analyst Intern role to apply analytical skills to real-world business problem
Meruyert Boranbay
@meruyert
Astana IT University
I'm a backend and full-stack developer based in Astana, working mainly with Go, Python, Typescript and React. Right now I build AI automation and AI-powered document-generation systems in fintech, and earlier I developed an ML-based video-monitoring backend for real-time incident detection. I'm especially interested in MLOps, cloud architecture, and scaling AI-driven infrastructure.
Ilya Shapovalov
@yagaMI-Reverse
AI/LLM engineer from Kostanay. I build production RAG systems, LLM agents and automation pipelines — and I measure them instead of guessing. Examples: GraphRAG against vanilla RAG on the same corpus (+20 points on multi-hop questions), a document pipeline turning a 116-page tender into 745 requirements with page references and confidence, recall 92.2% on a hand-labeled gold set, and a deterministic evaluation harness with no LLM-as-judge (accuracy 85 to 100%). Stack: Python, FastAPI, LangGraph, Qdrant, Neo4j, PostgreSQL, Docker, Kubernetes. Open to remote work and contract projects.
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