Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I’m Abdelaziz Ouayazza, an AI engineer and data scientist focused on building end-to-end, trustworthy machine learning pipelines for complex scientific and real-world data. My work spans deep learning for data processing automation, semantic segmentation for 2D analysis, and robust evaluation against classic signal-processing baselines and strong dataset benchmarks. I also develop production-ready systems—designing data/LLM pipelines with RAG and continuous improvement loops, deploying services with FastAPI and Grad.io, and containerizing applications for cloud and orchestration environments like Docker and Kubernetes. Alongside technical development, I value clear communication, repeatable experimentation, and solid engineering practices that turn models into practical tools.…I’m Abdelaziz Ouayazza, an AI engineer and data scientist focused on building end-to-end, trustworthy machine learning pipelines for complex scientific and real-world data. My work spans deep learning for data processing automation, semantic segmentation for 2D analysis, and robust evaluation against classic signal-processing baselines and strong dataset benchmarks. I also develop production-ready systems—designing data/LLM pipelines with RAG and continuous improvement loops, deploying services with FastAPI and Grad.io, and containerizing applications for cloud and orchestration environments like Docker and Kubernetes. Alongside technical development, I value clear communication, repeatable experimentation, and solid engineering practices that turn models into practical tools.

Abdelaziz Ouayazza

AI Engineer, Data Scientist, Software Engineer, +1





I’m Abdelaziz Ouayazza, an AI engineer and data scientist focused on building end-to-end, trustworthy machine learning pipelines for complex scientific and real-world data. My work spans deep learning for data processing automation, semantic segmentation for 2D analysis, and robust evaluation against classic signal-processing baselines and strong dataset benchmarks. I also develop production-ready systems—designing data/LLM pipelines with RAG and continuous improvement loops, deploying services with FastAPI and Grad.io, and containerizing applications for cloud and orchestration environments like Docker and Kubernetes. Alongside technical development, I value clear communication, repeatable experimentation, and solid engineering practices that turn models into practical tools.…I’m Abdelaziz Ouayazza, an AI engineer and data scientist focused on building end-to-end, trustworthy machine learning pipelines for complex scientific and real-world data. My work spans deep learning for data processing automation, semantic segmentation for 2D analysis, and robust evaluation against classic signal-processing baselines and strong dataset benchmarks. I also develop production-ready systems—designing data/LLM pipelines with RAG and continuous improvement loops, deploying services with FastAPI and Grad.io, and containerizing applications for cloud and orchestration environments like Docker and Kubernetes. Alongside technical development, I value clear communication, repeatable experimentation, and solid engineering practices that turn models into practical tools.

Available to hire

I’m Abdelaziz Ouayazza, an AI engineer and data scientist focused on building end-to-end, trustworthy machine learning pipelines for complex scientific and real-world data. My work spans deep learning for data processing automation, semantic segmentation for 2D analysis, and robust evaluation against classic signal-processing baselines and strong dataset benchmarks.

I also develop production-ready systems—designing data/LLM pipelines with RAG and continuous improvement loops, deploying services with FastAPI and Grad.io, and containerizing applications for cloud and orchestration environments like Docker and Kubernetes. Alongside technical development, I value clear communication, repeatable experimentation, and solid engineering practices that turn models into practical tools.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Arabic

Fluent

French

Advanced

Work Experience

AI Engineer & Researcher at Institute of Molecular Chemistry of Reims (CNRS UMR 7312)

February 1, 2026 - July 1, 2026

Built an end-to-end NMR data processing pipeline from Bruker FID files to automated peak detection using TopSpin, NMRGlue, and Python. Generated realistic synthetic HSQC spectra with automated ground-truth annotation for supervised learning. Designed, trained, and optimized a U-Net semantic segmentation model (U-NetPicker) for automated 2D NMR peak picking; developed and benchmarked classic feature-based methods including Harris, SUSAN, LoG, DoG, Hessian, local maxima, and the electrostatic model combined with watershed segmentation and DBSCAN clustering. Evaluated methods on synthetic and experimental datasets (HMDB) using Dice score, precision, recall, and F1-score; conducted a literature review on AI for NMR spectroscopy and automated peak picking in multidimensional NMR.

AI Researcher - Multi-agent systems & LLM evaluation

January 1, 2024 - Present

Designed an autonomous LLM evaluation and alignment pipeline integrating deep evaluation, RAGAS, human feedback, continuous fine-tuning, and MLflow for MLOps tracking. Built an AI multi-agent system for SOC optimization data science tasks and optimized threat detection, incident prediction, and automated reporting workflows.

Medical QA Assistant (QLoRA Fine-Tuning of TinyLlama-1.1B-Chat) at Deployed via FastAPI/Gradio and published on Hugging Face

January 1, 2024 - Present

Fine-tuned TinyLlama-1.1B-Chat using QLoRA on a dataset of 10,000 medical question-answer pairs. Deployed the model through FastAPI and Gradio, published it on Hugging Face, and containerized the application using Docker and Kubernetes.