MLOps Architect

Job description

We are seeking a highly skilled **MLOps Architect** to join our team and lead the design and implementation of robust and scalable MLOps solutions on the Azure cloud platform. The ideal candidate will have a strong background in cloud-based machine learning operations, DevOps practices, and data engineering, with a focus on unstructured data in industries such as automotive. **Responsibilities** - Design and maintain scalable MLOps infrastructure within the Azure ecosystem - Develop and optimize CI/CD pipelines for machine learning model deployment and integration - Utilize Docker for containerization and orchestrate containers with Kubernetes - Implement and manage complex data pipelines for processing large-scale unstructured data - Collaborate with data scientists and data engineers to streamline model lifecycle processes - Leverage Azure ML services such as Azure Databricks and Azure ML Workspace for end-to-end workflows - Ensure reliability, scalability, and performance of machine learning systems in production environments **Requirements** - 12 to 16 years of overall experience, with strong proficiency in Python for scripting and automation - Hands-on experience with Azure Machine Learning, Azure Databricks, and a strong understanding of Azure cloud services - Practical expertise in Docker, CI/CD pipelines using tools like Azure DevOps, and Kubernetes orchestration - Proven ability to design and manage complex data pipelines, particularly for unstructured data - Azure certifications (e.g., Azure Data Engineer, AI Engineer, or DevOps Engineer) - Competency with Infrastructure as Code tools like Terraform or ARM templates - Understanding of monitoring and logging tools for ML models, such as Prometheus, Grafana, or Azure Monitor - Advanced degree in Computer Science, Engineering, Data Science, or a related field is preferred