Nidal Abu-Raed

Evaluated and validated AI system outputs across multiple use cases, performed data annotation and quality assurance to support the training and improvement of machine learning models, and conducted systematic testing of AI-driven features to enhance model robustness and performance. Collaborated with distributed teams to follow evolving project guidelines and maintain high standards of data quality and evaluation consistency.

Spearheaded the development and deployment of AI-driven solutions, optimizing data pipelines and ML model performance for scalable, high-availability systems. Implemented deep learning models using TensorFlow and Keras (including LLMs) for computer vision and NLP, designed predictive analytics for telecom, and led CI/CD pipelines with Jenkins and GitLab. Deployed AI models on AWS and Azure; applied MLOps with Kubeflow, MLflow, and Docker to streamline workflows.

Trained and evaluated large language models by designing high-quality prompts and validating model outputs to improve accuracy, coherence, and alignment with user intent. Performed data annotation, curation, and quality control on diverse datasets, analyzed model performance, identified failure patterns, and contributed to iterative improvements in model behavior and reliability.