MLOps Engineer

📋 Description

• Build and maintain ML pipelines — design, automate, and operationalise end-to-end pipelines for
• Own model deployment and serving infrastructure — manage containerised model endpoints, versioning
• Implement monitoring and observability — establish model performance monitoring, data drift
• Govern LLM usage across CUBE's platform — take ownership of LLM provider relationships (OpenAI
• Manage the LLM gateway and prompt versioning — maintain tooling such as LangSmith or Helicone for
• Support experiment tracking and model registry — ensure that experiments are reproducible, models

🎯 Requirements

• 3-4 years of experience in Machine Learning, Azure, deployment, pipeline
• Experience with Azure-native tooling including Azure AI Foundry and Azure Machine Learning
• Experience with containerised model deployment, versioning, traffic management, and rollback
• Experience in model monitoring, data drift detection, and alerting frameworks
• Experience with LLM providers such as OpenAI, Azure OpenAI, and Anthropic
• Experience with LLM gateway and prompt versioning tools (LangSmith, Helicone)