Rasul Kazumovi

Seeking a full-time remote role as a Machine Learning Engineer or Software Engineer to build AI-powered applications and scalable software solutions. Interested in opportunities involving Python, machine learning, deep learning, LLMs, and modern software engineering practices. Passionate about solving complex technical problems, continuous learning, and delivering impactful products. Machine Learning Engineer and Software Engineer with a BSc in Mathematics — building AI systems where scientific rigor meets production engineering. Most ML engineers come from one of two backgrounds: strong software engineering or strong mathematics. I come from both. My mathematics degree covers Linear Algebra, Partial Differential Equations, Stochastic Calculus, Numerical Methods, Functional Analysis, Optimization Theory, Probability Theory, and Real Analysis. My software engineering background spans 2+ years building production AI applications, LLM-powered systems, and automation workflows across remote Agile teams. That combination is rare — and it defines the kind of problems I can solve. I specialize in Physics-Informed Neural Networks (PINNs) — neural networks that learn by satisfying physical laws rather than memorizing labeled data. My implementations have achieved 99.77% accuracy solving the heat equation and extended to Navier-Stokes equations for aerodynamic flow modeling — problems most ML engineers cannot formulate. At FloLabs Innovations Group, I engineered LLM-powered AI agent pipelines and intelligent automation workflows in Python and Next.js — eliminating manual processes across onboarding, email campaigns, and internal operations, and delivering production-grade chatbot functionality to end users. At Novator, I built scalable web applications achieving 30% faster page load times and improved cross-device performance, while elevating team code quality by 20% through systematic mentorship and code reviews across a 10-member engineering team. Before engineering, I spent 5 years teaching advanced Mathematics and Physics — improving student academic performance by up to 75%. Teaching forced genuine mastery of the mathematical foundations that now directly power my ML work. I am building toward ML Engineering roles at the frontier of science and technology — specifically in: • Scientific Machine Learning & Physics-Informed Neural Networks • Aerospace, climate technology & energy systems • Large Language Models & production AI systems • Computational physics applied to real-world engineering The intersection of deep mathematics, physics intuition, and software engineering is where I do my best work — and where very few engineers operate. If you are a founder, researcher, or engineering leader building at the intersection of AI and hard science — aerospace, climate tech, energy, biotech, or computational physics — I would like to talk.