MLOps Engineer

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients. 

Why us?

🌍 CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally.

🚀 In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead.

🌱 We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one.

🌐 With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success.

💡 We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.

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.

You'll sit at the intersection of data engineering, software engineering, and machine learning, translating the work of data scientists and AI architects into robust, scalable production systems. In a platform undergoing significant consolidation following CUBE's acquisitions, this role has real scope: you'll be helping to establish MLOps as a discipline from the ground up, not inheriting a fully formed practice.

This role reports to the Lead Data Scientist and works closely with the broader Data and AI Engineering team. You'll be expected to bring rigour and ownership to everything from pipeline automation and model deployment to LLM observability and provider governance.

We're looking for someone who cares about operability as much as capability — who understands that a model no one can monitor, retrain, or roll back isn't production-ready, regardless of its benchmark scores.

Build and maintain ML pipelines — design, automate, and operationalise end-to-end pipelines for model training, evaluation, and deployment using Azure-native tooling including Azure AI Foundry and Azure Machine Learning