You make machine learning models dependable to train, deploy and monitor in production.
Key Responsibilities
- Build automated training, testing and deployment pipelines for models
- Run model serving on containers and Kubernetes
- Monitor model performance, drift and infrastructure cost
- Manage experiment tracking, model registries and data versioning
- Work with data scientists and DevOps engineers on shared standards
Required Skills
- Experience with Python, Docker and CI/CD
- Knowledge of a cloud platform such as AWS or Azure
- Understanding of the machine learning lifecycle
- Monitoring and troubleshooting skills
Nice to Have
- Experience with MLflow, Kubeflow or SageMaker
- Infrastructure as code with Terraform
Tools and technologies: Python; Docker and Kubernetes; CI/CD; MLflow or similar (nice to have)
How to prepare
- Be ready to describe how you would detect and respond to model drift
- Revise container basics and deployment pipelines