MLOps Engineer

About the role

We're looking for an MLOps Engineer to own the end-to-end lifecycle of machine learning models in production at CUBE. As our AI capabilities grow — spanning both proprietary ML models and large language model integrations — we need someone who can build the infrastructure and operational discipline that keeps those systems reliable, observable, and cost-effective.

What you'll do

• Sit at the intersection of data engineering, software engineering, and machine learning, translating the work of data scientists and AI architects into robust
• Be helping to establish MLOps as a discipline from the ground up, not inheriting a fully formed practice
• Be expected to bring rigour and ownership to everything from pipeline automation and model deployment to LLM observability and provider governance
• Build and maintain ML pipelines — design, automate, and operationalise end-to-end pipelines for model training, evaluation
• Own model deployment and serving infrastructure — manage containerised model endpoints, versioning, traffic management, and rollback mechanisms across environments

What you need

• 3–4 years in data science & ai
• MLOps
• Machine learning
• LLMs
• Azure
• CI/CD
• Test automation
• Any graduate

Good to have

• Terraform