I am a Machine Learning Engineer focused on building efficient, scalable, and practical AI systems. I specialize in developing models that can learn from structured and unstructured data, with a strong emphasis on performance, optimization, and real-world deployment.

My experience includes data preprocessing, feature engineering, model training, and evaluation across various machine learning and deep learning frameworks. I work extensively with Python, leveraging libraries such as scikit-learn, PyTorch, and NumPy to design and implement solutions.

I am particularly interested in lightweight and offline-capable models, ensuring systems can run efficiently even in resource-constrained environments. I also explore building intelligent systems that can process and learn from documents like PDFs and provide accurate, context-aware responses.

I value clean architecture, reproducibility, and fast iteration cycles, and I often integrate automation and scripting to streamline workflows. My goal is to turn data into actionable intelligence through robust and reliable machine learning systems.