General familiarity with applied machine learning concepts, including multilayer perceptrons, convolutional neural networks (CNNs), recurrent architectures such as LSTMs, transformer models, supervised and unsupervised learning methods, reinforcement learning methods, and statistical or graphical models. This role supports research and development in areas including Large Language Models (LLMs), natural language processing (NLP), natural language understanding (NLU), natural language generation (NLG), AI‑based decision-making, sensor fusion, data fusion, computer vision, and image processing, with a focus on responsible and explainable AI.