Ml Developer / Mlops Engineer

**A Snapshot of Your Day**
The day of an ML Developer/MLOps Engineer starts the day by reviewing model performance metrics and identifying any drift in production models.
A team meeting follows, where updates on model training and evaluation pipelines are shared.
The engineer then works on converting 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 for improvements based on stakeholder feedback.
**How You'll Make an Impact**
- Develop and maintain robust MLOps CI/CD pipelines for model training, testing, versioning, and deployment.
- Package and serve models as containerized, scalable API endpoints (Docker, Azure Container Services, APIM).
- Availability to travel and visit proje...