The ML DevOps Engineer will focus on building and maintaining the infrastructure and pipelines necessary for seamless ML model deployment and monitoring.
Design and implement automated infrastructure setups using tools like Terraform, CloudFormation, or Kubernetes.
Build and maintain CI/CD pipelines that incorporate model training, validation, and deployment.
Integrate data pipelines, model training, and inference processes with the existing infrastructure.
Create and manage environments (Development, Testing, Staging and Production).
Implement monitoring and logging solutions to track performance, data drift, and versioning.
Set up and manage containerisation using Docker for ML model deployment.
Automate MLOps processes to improve efficiency and reliability.
Collaborate with the ML Engineer to ensure smooth integration of models into production environments.
