Rasul Kazumovi

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.