saqeeb ahmed

I've built an end-to-end AI pipeline for industrial process data at TU Chemnitz, transforming 672,744 sensor observations into explainable material-removal predictions with R² = 0.96. I connected temporal feature engineering, Random Forest and XGBoost models, SHAP explanations, and a semantic knowledge graph to support reliable sensing-to-decision workflows.

At Goethe University, I shipped three production React.js and Redux applications used by 200+ students, including a digital learning tool for NoSQL data models. I also built and deployed interactive dashboards for live and simulated sensor inference, process-safety classification, and model-agreement monitoring.

I've worked across front end, back end, APIs, databases, CI/CD, and cloud tooling, collaborating with distributed agile teams to improve reliability, delivery speed, and user experience.