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
