Description:
We're looking for a DevOps Engineer with strong MLOps experience to help operationalize AI and Machine Learning models across the business. You will build and manage the cloud, infrastructure, and pipelines that allow Data Scientists and ML Engineers to deploy models into production safely, reliably, and at scale.
This is a senior technical role where you will work closely with AI Engineers, Data Scientists, Cloud Architects, and business teams. You will also have the opportunity to mentor junior engineers and help shape the future of AI engineering within the organisation.
What You Will Do
Build and maintain CI/CD pipelines for AI/ML workloads.
Implement DevOps practices for the full ML lifecycle (experiment train deploy monitor retrain).
Deploy machine learning models in AWS, Azure, or GCP.
Use containerization and orchestration tools such as Docker and Kubernetes.
Manage AI/ML platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI.
Monitor models in production and set up alerts for performance, drift, and retraining.
Troubleshoot issues across infrastructure, applications, and pipelines.
Provide architectural input and help design scalable AI/ML environments.
Document processes, build reusable frameworks, and support cross-functional teams.
Mentor junior engineers and support continuous improvement in the team.