Develop and deploy supervised and unsupervised machine learning models for manufacturing applications, including anomaly detection, classification, and regression. Responsibilities include data preprocessing, model selection, optimization, scalability improvements using Dask, Ray, and Optuna, and collaboration with cross-functional teams. The role requires expertise in tree-based and neural network algorithms, custom loss functions, PyTorch, Python, and the full machine learning lifecycle.

Lead the architecture and implementation of scalable, maintainable software components for an AI manufacturing platform. Develop automated testing strategies, benchmark system performance, identify bottlenecks, propose optimizations, and collaborate cross-functionally on feature integration. The role also involves debugging, cloud infrastructure, DevOps practices, containerization, and frontend framework collaboration.