MLOps engineer

Pay in the destination countries

Monthly, in US dollars. Each country is an Imora admission package — open one for that route's fees, language requirement and deadline.

What does the day look like?

Your day is spent moving a model from a notebook to production and keeping it there. You build deployment pipelines with GitHub Actions, Jenkins or GitLab, containerise services with Docker and run them on Kubernetes, orchestrate retraining with Airflow or Kubeflow, track experiments and register versions with MLflow or Weights and Biases, serve models on SageMaker, Vertex AI, Azure ML or Triton, monitor data drift and quality with Prometheus, Grafana or Evidently, write infrastructure as code with Terraform, and own cost, access control and audit trails on AWS, Google Cloud or Azure.

The glamorous part is the minority: you do not train architectures from scratch on a normal day, and most model artefacts arrive from a data scientist or a fine-tuning job. The majority is platform work: unblocking failed builds, reading logs, patching environments, writing runbooks, answering data scientists' tickets, and proving to security reviewers that an endpoint is safe. The paper schedule is a 40-hour week, but wherever models serve live traffic the real schedule is set by releases and incidents, with on-call rotations, evening pages and weekend retraining failures as part of the job.

Where does this lead?