ML Developer / MLOps Engineer

A Snapshot of Your Day

The day of an ML Developer/MLOps Engineer starts by reviewing model performance metrics and identifying drift in production models. A team meeting follows, sharing updates on model training and evaluation pipelines. The engineer then converts Jupyter notebooks into reproducible training pipelines, ensuring proper version control.

After lunch, they package and serve a new machine learning model via Azure ML Endpoints, collaborating with data engineers to manage data and feature pipelines. The day concludes with documenting the integration process and planning improvements based on stakeholder feedback.

How You’ll Make An Impact

• Build, deploy, and operate AI applications as production-grade microservices on Azure (App Services, Container Apps, AKS).
• Develop and maintain robust MLOps CI/CD pipelines for model training, testing, versioning, and deployment.
• Package and serve models as containeri...