Hire the Best Machine Learning Engineers in Bishkek, KG

I help founders turn an idea into a working product fast. Not a fragile prototype — a real MVP that ships in weeks and won't need a rewrite when users arrive. I build the whole thing end to end: frontend (React / Next.js), backend (FastAPI, PostgreSQL), and AI features like RAG, agents and semantic search when the product needs them. I also build in the open. My open source projects show how I think about engineering: - Forze: a Python toolkit for building backend services with clean, testable architecture - Bloomery: a compiler that turns declarative data specs into ready-to-run SQLMesh, dbt and Cube artifacts - Morzer: a CLI for deploying and managing self-hosted products: install, update, rollback, backup What I build - MVPs and SaaS, end to end. Frontend, backend, database, auth, Stripe payments, deployment. First we cut the scope - most founders build too much in version one. Then I ship fast, on an architecture that can grow. - Frontend. React / Next.js, TypeScript, Tailwind. Apps, dashboards, admin panels that feel finished even when the product is two weeks old. - Backends that hold up. FastAPI, PostgreSQL, Kafka, durable background jobs (Temporal, Inngest) - so a long job survives a restart instead of losing its progress. - AI features that work in production. RAG, LLM agents with typed guardrails, semantic search, document pipelines. Built as a reliable feature - not a demo that falls apart with real data. When something already works - but badly If your product works in the demo but breaks with real users, I can help. I find the real cause behind random failures - in payments, background jobs, or AI features - and fix it properly, so you stop losing users to bugs nobody can reproduce. How I work In small, visible steps. Usually you have something deployed and clickable within the first week, so you see progress early instead of waiting for a big reveal. I keep the code typed and tested, and I'm honest about trade-offs: I'll tell you which shortcuts are fine for an MVP, and which ones will hurt the product in three months. My testing approach is open source as Forze, a Python framework I maintain. Background MSc in Applied Physics and Mathematics, nine published papers, years of large-scale simulations on HPC clusters. That's where my habit of reproducible results comes from. Useful if your product involves heavy computation or scientific data.