MLOps Engineer (Cloud)

Role Overview

We are looking for an MLOps Engineer with solid cloud experience to help build, deploy and maintain Machine Learning solutions in production. The right person combines strong engineering foundations with a passion for automation, cloud architectures and operational excellence.

You will collaborate closely with Data Scientists, Data Engineers, Architects and Platform teams to ensure that AI models are delivered in a scalable, reproducible and secure way.

Key Responsibilities

• Design, build and maintain end to end ML pipelines for training, validation and deployment
• Implement CI CD practices for the entire model lifecycle
• Automate workflows using tools such as Airflow, Kubeflow, MLflow or Vertex Pipelines
• Deploy models on cloud platforms like AWS Sagemaker, Azure ML or GCP Vertex AI
• Implement monitoring, observability and alerting for models in production
• Work with Data Scientists to optimise performance, datasets and reproducibility
• Manage infrastructure as code with Terraform or CloudFormation
• Ensure compliance with data governance, security and model versioning policies
• Optimise reliability, performance and cloud cost of ML platforms
• Document architectures, processes and best practices

Requirements

Technical Skills

Soft Skills

Nice to Have