Hind Aaibid

AI Engineering Intern working on predictive maintenance solutions for electric vehicle traction motors.

• Developed end-to-end machine learning and deep learning pipelines for fault detection using vibration signal datasets.
• Benchmarked CNN-1D, LSTM, BiLSTM, XGBoost, Random Forest, SVM, and KNN models.
• Implemented Autoencoder and Variational Autoencoder (VAE) models for anomaly detection.
• Performed feature engineering using rolling-window techniques for time-series analysis.
• Improved model robustness under noisy conditions through regularization and data processing techniques.
• Conducted cost-benefit analysis of predictive maintenance strategies to evaluate operational savings and downtime reduction.
• Technologies: Python, PyTorch, TensorFlow, Scikit-Learn, Pandas, and MLflow.

Conducted research in machine learning and deep learning for the analysis of EEG signals related to Alzheimers Disease and Frontotemporal Dementia.

• Performed literature review and comparative analysis of machine learning and deep learning approaches for EEG-based dementia detection.
• Worked with the OpenNeuro ds004504 dataset containing EEG recordings from Alzheimers Disease, Frontotemporal Dementia, and healthy subjects.
• Contributed to data analysis, model evaluation, scientific writing, and publication preparation.
• Co-authored the paper EEG-based Machine and Deep Learning Approaches for Alzheimers and Frontotemporal Dementia: A Systematic Review of the OpenNeuro ds004504 Dataset, presented at IRASET 2026 (IEEE/Scopus indexed).

Remote AI Developer responsible for developing AI solutions for animal shelters, municipalities, and pet management services.

• Developed a multilingual AI Assistant using GPT-4o, LangChain, and FAISS to answer questions about municipal animal regulations.
• Built Retrieval-Augmented Generation (RAG) pipelines with automated document ingestion and source-based responses.
• Developed an Animal Facial Recognition System using YOLOv8, ONNX, FastAPI, and FAISS to help identify and reunite lost pets with their owners.
• Integrated AI services with shelter management and municipal platforms.
• Improved pet identification, animal welfare operations, and information accessibility through AI-driven solutions.
• Collaborated with software development teams to deploy production-ready AI applications.

Completed a software development internship focused on administrative process digitalization.

• Designed and developed a trainee management system using Laravel.
• Implemented data visualization features using Google Charts.
• Assisted in database management and system testing.
• Contributed to improving efficiency in trainee administration and reporting.

Completed a software development internship focused on administrative process digitalization.

• Designed and developed a trainee management system using Laravel.
• Implemented data visualization features using Google Charts.
• Assisted in database management and system testing.
• Contributed to improving efficiency in trainee administration and reporting.