I pursue AI/ML with one constraint: knowledge should translate into systems that genuinely benefit people.
I’m a Computer Engineering student with an AI/ML focus, graduating in 2027. I treat machine learning as an engineering discipline. Ideas must survive real data, real users, and real production constraints.
- Build and ship end-to-end ML systems, from data and modeling to deployment and monitoring.
- Lead practical projects involving LLMs, human-in-the-loop learning, model efficiency, and reproducible MLOps.
- Write technical explanations that connect intuition, mathematics, and implementation.
- Ouro – Self-healing conversational AI: Production-ready chatbot built on LLaMA with human-in-the-loop learning, LoRA fine-tuning, efficiency-focused quantization, and an MLflow + Docker MLOps pipeline.
- Breast Cancer Classification: Logistic Regression, SVM, and XGBoost with reproducible EDA, stratified cross-validation, and strong interpretability. ROC-AUC around 0.99.
- DEPI Generative AI projects: Team-based work on generative models and LLM systems.
- Strengthen foundations in probability, statistics, and advanced linear algebra.
- Ship reproducible ML projects with clear engineering ownership.
- Publish applied research and contribute to open-source ML tooling.
- Deep Learning Specialization (Coursera)
- Papers on Explainable AI and model reliability
- Practical generative AI tooling and deployment workflows
- DEPI Generative AI Diploma (completed)
- Machine Learning Specialization – Andrew Ng (completed)
- Huawei HCIA-AI (certified)
- Deep Learning Specialization – Andrew Ng (in progress)
- NVIDIA DLI – Foundations of Deep Learning
- 📫 pierre.ramez10@gmail.com
- Open to collaborations on serious AI/ML research and production projects