Alisher-Polat Kurmanayev

Machine Learning Engineer focused on recommender systems, applied machine learning, and production AI infrastructure. Currently building a next-generation music streaming platform at Freedom Music, designing recommendation systems and ML infrastructure from the ground up. Working across the full lifecycle of machine learning products, from data collection and feature engineering to retrieval, ranking, experimentation, and large-scale serving. Experience includes: * Designing recommendation systems using multi-stage Retrieval and Ranking architectures * Building vector-search pipelines with embedding-based retrieval and candidate generation * Developing ranking models for personalized content recommendations * Designing event-tracking and user analytics systems for ML datasets * Solving cold-start problems for users and content in large-scale recommendation platforms * Building FastAPI microservices and containerized ML infrastructure * Developing LLM applications, multi-agent systems, RAG pipelines, and workflow automation * Fine-tuning and deploying open-source language models Interested in: * Recommender Systems * Information Retrieval * Ranking and Personalization * Applied Machine Learning * ML Infrastructure and MLOps * Agentic AI Systems * Large-Scale Retrieval Architectures * Experimentation and A/B Testing Tech stack: Python, FastAPI, PyTorch, CatBoost, LightGBM, FAISS, Qdrant, PostgreSQL, Redis, Docker, Linux, LangGraph, LangChain, vLLM, scikit-learn, Prometheus, Grafana, Langfuse.

I am looking to connect with professionals working in machine learning, AI systems, recommendation systems, and data science. I am interested in knowledge sharing, technical discussions, research collaborations, mentorship opportunities, and networking with people building real-world AI products. I would also like to stay informed about industry trends, events, and opportunities for collaboration.

I can contribute practical experience in machine learning engineering, recommendation systems, LLM applications, multi-agent systems, RAG architectures, and AI infrastructure. I am happy to share technical knowledge, discuss system design, collaborate on research and open-source projects, provide feedback on ML projects, and support community events, workshops, and knowledge-sharing initiatives.