• Design end-to-end machine learning solutions leveraging Azure ML services, considering factors such as scalability, performance, security, and cost efficiency.
• Strong understanding of the following: Cloud native architecture principles, Modern architecture techniques
• Strong understanding of machine learning principles, data preprocessing, and feature engineering.
• In-depth experience architecting complex Azure public/private Cloud platform solutions (PaaS, SaaS, IaaS);
• Architect cloud-based infrastructure and resources required for training deploying, and managing machine learning models using Azure resources like Azure Databricks, Azure Kubernetes Service (AKS), Azure VMs, etc.
• Integrate diverse data sources and preprocess data for training and inference, using Azure Data Factory, Azure Data Lake, or other relevant Azure services.
• Deploy models to production environments using Azure ML deployment technologies like Azure ML Service, Azure Functions, or AKS, and establish monitoring mechanisms for model performance, drift, and health.
• Implement security measures, access controls, and data protection protocols in according to organizational policies and regulatory requirements.
• Continuously optimize machine learning pipelines and models for performance, cost, and resource utilization using techniques like distributed computing and model quantization.
• Excellent problem-solving and communication skills.