I design, train, and deploy machine learning models for time series forecasting, computer vision, and graph based simulations using PyTorch, PyTorch Lightning, TensorFlow, and PyTorch Geometric, and adapt quickly to new tools as needed. My work spans the full MLOps lifecycle, including data ingestion, processing, training, evaluation, and registry management, using Docker, MLflow, and DevContainers to deliver reproducible, production ready pipelines. I build both self hosted and cloud based solutions and develop automated inference APIs to streamline deployment and collaboration.
My foundation in software engineering, delivering modular architectures, real time communication systems, and payment integrations, directly enhances my AI/ML work by enabling scalable system design, performance optimization, and robust deployment workflows.
