Machine Learning Ops and Data Engineer

About the role

This position blends data platform engineering with MLOps duties, ensuring that analytical data pipelines and production‑ready machine‑learning models run reliably. You will work closely with Data Science, Engineering, BI, Risk and IT teams to deliver scalable, well‑documented solutions that meet business needs.

Key responsibilities

• Deploy, monitor and maintain machine‑learning models in production environments.
• Support and optimise cloud‑based analytical, reporting and ML infrastructure, primarily on Azure.
• Design, build, troubleshoot and optimise ETL pipelines for reporting, analytics and ML use cases.
• Automate model deployment, updates, scaling and recurring data‑processing tasks.
• Implement monitoring and quality checks for data pipelines and ML models.
• Collaborate with cross‑functional teams to align data and model requirements with production standards.
• Maintain technical documentation for data processes, model deployments and operational procedures.

Required profile

• Minimum 2 years of experience in data engineering and machine‑learning operations.
• Strong Python programming skills and solid SQL knowledge.
• Good understanding of databases, data‑warehouse concepts and ETL processes.
• Familiarity with the machine‑learning lifecycle and model operationalisation.
• Experience with cloud‑based analytical solutions, preferably Microsoft Azure.
• Knowledge of DevOps practices, CI/CD pipelines and version control.

Required skills

• Python
• SQL
• Microsoft Azure
• Docker
• CI/CD (e.g., Azure DevOps, GitHub Actions)
• Version control (Git)
• scikit‑learn
• XGBoost
• Data‑warehouse tools