As an AI Engineer on the Data Science team, you will play a key role in productionizing machine learning models, building robust pipelines, and enhancing the overall AI platform. This role requires hands-on experience with Azure, Docker, and Azure Kubernetes Service (AKS), as well as strong knowledge of cloud-native MLOps best practices. Responsibilities • Design and implement scalable, cloud-native ML pipelines for production AI solutions. • Collaborate with data scientists to operationalize ML models from prototypes to production. • Manage deployment of ML models using Azure Machine Learning and AKS. • Develop, containerize, and orchestrate services using Docker and Kubernetes. • Optimize cloud data and compute architectures to ensure cost-effective and reliable deployments. • Implement robust monitoring, logging, and CI/CD practices to support AI operations (MLOps). • Work closely with enterprise cloud architects to align AI solutions with our client's infrastructure standards. • Contribute to the evolution of the best practices around AI/ML systems in production environments.