Keeps models running reliably in production. A natural switch for DevOps and backend engineers.
An MLOps engineer keeps machine learning models running reliably in production. If ML engineers build the models and data scientists design the experiments, MLOps engineers build the infrastructure, pipelines and monitoring that make it all work at scale.
This is a natural career path for DevOps, platform and backend engineers. The skills transfer directly — Docker, Kubernetes, CI/CD, cloud infrastructure, monitoring, alerting. You add ML-specific knowledge (model serving, data drift, feature stores, training pipelines) on top of your existing platform expertise.
Maintain existing pipelines, write deployment scripts, set up monitoring. Learn the ML-specific parts while leveraging your existing infra skills.
Design and build ML platforms. Own the model deployment lifecycle. Optimise cost and performance.
Architect the ML platform for the organisation. Define standards for model deployment, monitoring and governance.
Set the technical vision for the ML platform. Influence tooling decisions, vendor choices, and team structure across engineering.
Already working in another field? Here is how your background maps.
The most natural switch. Learn model serving (TorchServe, vLLM), model monitoring (drift detection), and feature stores. Your existing Kubernetes, CI/CD and monitoring skills are 70% of the job already.
Learn Docker and Kubernetes if you do not know them, then model serving and ML pipeline tools (MLflow, Kubeflow). Your production systems experience transfers directly.
Learn the ML-specific managed services on your cloud platform (SageMaker, Vertex AI). Your cloud infrastructure expertise gives you a strong foundation.
