People in DSML Kazakhstan

Middle Computer Vision / ML Engineer. I build real-time video and audio analytics systems: object detection and tracking, face detection and recognition, appearance attributes, pose-based aggression detection, and ASR with Whisper (ru, en, kk). MLOps: ONNX, TensorRT, Docker, FastAPI, DVC, MLflow.

AI/ML engineer with hands-on experience in computer vision, data preparation, model training, evaluation, and end-to-end pipeline development. Enjoy learning new things!

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.

ML engineer and researcher with 2.5+ years of experience in computer vision and generative AI. I specialize in diffusion models (fine-tuning FLUX, ControlNet, LoRA), portrait animation pipelines, video generation, and the development of generative CV systems—from researching SOTA methods to production deployment.

I am an ELLIS PhD student at CISPA focused on foundation models, multimodal learning, and activation steering methods under supervision of Prof. Dr. Mario Fritz. My work spans promptable segmentation (SAM), domain generalization, and model soups. I have BMVC 2025, MICCAI 2024, and a Scientific Data 2024 papers, plus open-source contributions and dataset curation. I enjoy scaling experiments, careful evaluation/ablation, and building usable research code.

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.