An MLOps Engineer is responsible for managing the lifecycle of machine learning models, ensuring they are deployed, monitored, and maintained effectively.
The MLOps Engineer supports the development, training, and deployment of machine learning models, and key skills involve CI/CD Pipelines, Model Deployment, Monitoring and Maintenance, Automation and Performance Optimization.
Requirements:
Relevant work experience in ML projects
Relevant work experience in technologies and frameworks used in ML, examples are: Apache Airflow, sklearn, MLFlow, TensorFlow
Knowledge of MLOps architecture and practices
Knowledge of data manipulation and transformation, e.g. SQL
Experience working in cloud environment (e.g. GCP)
Programming in Python
Experience with monitoring and observability (ELK stack)
Familiar with software engineering practices like versioning, testing, documentation, code review
Deployment and provisioning automation tools e.g. Docker, Kubernetes, OpenShift, CI/CD
