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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
Und