MLOps Engineer

About the role

CUBE is a global RegTech business delivering AI‑powered SaaS solutions for regulatory compliance. We are seeking an MLOps Engineer to own the end‑to‑end lifecycle of machine learning models in production.

Key responsibilities

• Design, automate, and operationalise end‑to‑end ML pipelines using Azure AI Foundry and Azure Machine Learning.
• Manage containerised model endpoints, versioning, traffic routing and rollback mechanisms across environments.
• Establish model performance monitoring, data‑drift detection and alerting frameworks.
• Govern LLM usage, maintain provider relationships (OpenAI, Azure OpenAI, Anthropic) and optimise token‑consumption costs.
• Maintain LLM gateway and prompt versioning tools such as LangSmith or Helicone.
• Support experiment tracking and model registry to ensure reproducibility and auditability.
• Champion CI/CD, infrastructure‑as‑code and automated testing for ML workflows.
• Monitor infrastructure spend, identify optimisation opportunities and ensure platform SLAs are met.

Required profile

• 3–4 years of experience in machine learning, MLOps and Azure deployment pipelines.
• Strong understanding of model operability, monitoring, retraining and rollback processes.
• Proven ability to collaborate with data scientists and engineers to translate requirements into reliable production systems.
• Experience with LLM integrations and provider management is a plus.

Required skills

• Azure AI Foundry
• Azure Machine Learning
• Azure cloud services
• Containerisation (Docker/Kubernetes)
• OpenAI, Azure OpenAI, Anthropic APIs
• LangSmith or Helicone
• CI/CD pipelines
• Infrastructure as Code
• Automated testing frameworks
• Experiment tracking tools