ML Ops Engineer Job (Remote)

Key responsibilities:

1. Build and optimize ML pipelines for efficient data ingestion, preprocessing, and feature engineering.
2. Utilise Azure and Azure ML to build, deploy, and manage machine learning workflows.
3. Develop and implement mechanisms to detect data drift, which refers to changes in the input data distribution over time that can impact model performance.
4. Utilise cloud services such as Azure and container orchestration tools like Docker to support ML workflows.
5. Ensure smooth integration of machine learning models into production environments with minimal downtime.
6. Design and implement MLOps frameworks and best practices to streamline model deployment and lifecycle management.
7. Collaborate with data scientists to transition POC projects into scalable and reliable production systems.
8. Oversee the deployment, monitoring, and maintenance of machine learning models in production environments.
9. Ensure the MLOps production systems are robust, scalable, and secure, with a focus on continuous integration and continuous deployment (CI/CD).

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