Skills — Adil Eddarif

Technical skills across deep learning, computer vision, RAG and LLM systems, MLOps, and backend engineering — PyTorch, YOLO, FAISS, FastAPI, MLflow, Docker.

AI engineer building production medical AI — retrieval-augmented clinical assistants and computer-vision triage pipelines that ship behind real safety constraints.

Artificial Intelligence

Deep learning (PyTorch, TensorFlow)
Computer vision (OpenCV, YOLO, ResNet)
Generative AI (LLMs, RAG, LangChain)
NLP (Hugging Face Transformers)
Evaluation (LLM-as-judge, golden datasets)

Data Engineering & MLOps

ETL and data pipelines
Experiment tracking (MLflow)
Containerization (Docker)
Cloud deployment (AWS)
Vector stores (FAISS, ChromaDB)

Software Engineering

Python, C++, Java, C#
REST APIs (FastAPI, Flask)
React and modern frontend
Git, Linux, testing (pytest)

Where these were used

Cheikh Zaïd Foundation: RAG, FAISS, BM25, FastAPI, PyTorch, ResNet50, YOLOv8, pytest.
GeoAP: PyTorch, YOLO11m, SAM1 ViT-H, SAHI, FastAPI, rasterio, Shapely, OpenCV, DVC, Roboflow, Docker, GitHub Actions.
Attijariwafa Bank: Python, Scikit-learn, MLflow, Docker, NLP, Flask.
Arteka: OpenCV, PyTorch, GANs, Python.

Skills — Adil Eddarif

Artificial Intelligence

Data Engineering & MLOps

Software Engineering

Where these were used

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