End-to-end ML engineering services tailored to your business needs.
Interactive dashboards (Plotly, Streamlit), exploratory data analysis, and actionable insights from complex datasets.
Classification, regression, time series forecasting. XGBoost, scikit‑learn, PyTorch – with clean, reproducible code.
Sentiment analysis, text classification, summarization. Fine‑tuning transformers (BERT) with LoRA for efficiency.
LoRA fine‑tuning, memory optimization, and modern training practices to maximize hardware utilization.
Experiment tracking (W&B), containerization (Docker), CI/CD basics. Currently learning multi‑container apps and GitHub Actions.
Real‑world ML systems with measurable impact on performance, efficiency, and scalability.
Research on efficient hate speech detection, conducted under supervision.
Categorized expertise with focus on production‑ready machine learning systems.
Multilingual communication and verified credentials in ML and data science.
Stanford Online / Coursera (Andrew Ng) · Issued Nov 2023
View credentialI'm Mahmoud El-Bahnasawi, an ML engineer focused on building production-ready systems, not just models. I combine AI with solid software engineering to ship work that actually runs.
I treat documentation and system design as core parts of the work, using tools like Mermaid to keep pipelines clear, reproducible, and easy to extend.
I start from the business problem. Before writing code, I ask: what value does this create?
My focus is end-to-end ML and MLOps: Docker, GitHub Actions, and Weights & Biases.
Have a project in mind? Reach out — let's build intelligent systems together.
Cairo, Egypt · Available for remote worldwide (part‑time)
Typically within 24 hours. For urgent matters, mention "URGENT" in subject.