preparation, feature engineering, model training, validation, deployment, monitoring, and continuous optimisation. Apply statistical analysis, experimentation, and data-driven decision-making to assess model performance and guide technical direction. Collaborate with engineers, product managers, applied scientists, and domain experts to translate ambiguous … Experience with cloud-native environments and container technologies, including Docker and Kubernetes. Experience developing model evaluation frameworks and AI quality measurement systems. Experience making architectural decisions across multi-service machine learning environments. Experience balancing model quality, operational cost, scalability, and performance requirements for production AI systems. Accommodation requests ...