The business world is drowning in AI wrappers and generic chatbots. True competitive advantage belongs to companies that own their algorithms. I specialize in Custom ML Model Development. With a background grounded in Python, TensorFlow, and Harvard CS50 fundamentals, I build, train, and deploy proprietary machine learning systems designed to solve your specific mathematical and operational problems.
From Data Engineering to Production MLOps
The hardest part of AI isn't the model; it's the data pipeline. My process for Custom ML Model Development starts at the foundation. I architect robust data extraction and cleaning pipelines using Pandas and SQL. Whether utilizing Scikit-learn for rapid predictive analytics or building deep neural networks in PyTorch, the architecture is tailored specifically to your enterprise constraints.
I have successfully applied Custom ML Model Development across highly volatile environments. In the financial sector, I engineer algorithmic trading systems (Expert Advisors) for XAUUSD and forex markets, utilizing momentum filters, ATR-based dynamic risk management, and deep learning to predict price action. In manufacturing, I deploy computer vision models that scan and verify CNC outputs, resulting in near-perfect QA automation. I deploy into live, production-grade MLOps pipelines—not just notebooks.
Security, Sovereignty, and Scale
When you rely on public APIs, you surrender your IP and risk data leaks. I focus on AI sovereignty—building local or secure-cloud models where you retain total ownership of your data and your weights. Systems that are secure, scalable, and fully integrated into your existing business software.
Frequently Asked Questions
What tech stack do you use for ML development?
I build primarily in Python, utilizing frameworks like TensorFlow, PyTorch, and Scikit-learn, backed by robust SQL databases and containerized Docker deployments.
Can you build algorithmic trading bots?
Yes. I develop and backtest advanced Expert Advisors using MQL5 and Python, integrating strict risk management rules designed to pass proprietary trading firm evaluations.
How do we transition a model from testing to live production?
I implement MLOps best practices, ensuring your model is hosted on scalable cloud infrastructure with continuous monitoring to prevent data drift and latency issues.