Azure MLops Engineer

The Role

We're looking for a skilled Azure MLOps engineer with a focus on automation to join our rapidly growing team. Your role will involve implementing, and maintaining scalable, secure cloud MLOps infrastructure on Azure, while ensuring the infrastructure's reliability and availability for data processing in the terabyte scale. You will collaborate with our Architects, DevOps consultant, Data scientist, forecaster and developers to provide a robust platform for our innovative applications.

Your responsibilities

• Collaborate with data scientists/forecaster to deploy machine learning models into production environments.
• Follow deployment strategies in place to ensure safe and controlled rollouts.
• Design and manage the infrastructure required for hosting ML models, including Azure cloud resources.
• Utilize containerization technologies like Docker to package models and dependencies.
• Establish Azure monitoring solutions to track the performance and health of deployed models. Set up logging mechanisms to capture relevant information for debugging and auditing purposes.
• Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.
• Optimize ML infrastructure for scalability and cost-effectiveness.
• Implement auto-scaling mechanisms to handle varying workloads efficiently such as parallel run
• Enforce security best practices to safeguard both the models and the data they process.
• Ensure compliance with industry regulations and data protection standards.
• Oversee the management of data pipelines and data storage systems required for model training and inference.
• Implement data versioning and lineage tracking to maintain data integrity.
• Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and system constraints.
• Collaborate with DevOps teams to align MLOps practices with broader organizational goals.
• Continuously optimize and fine-tune ML models for better performance.
• Identify and address bottlenecks in the system to enhance overall efficiency.
• Maintain comprehensive documentation for deployment processes, configurations, and system architecture.
• Communicate effectively with non-technical stakeholders, providing insights into the performance and impact of ML models