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
3-4 years of experience into Machine learning, Azure, deployment, pipeline is needed.
Own model deployment and serving infrastructure — manage containerised model endpoints, versioning, traffic management, and rollback mechanisms across environments
Implement monitoring and observability — establish model performance monitoring, data drift detection, and alerting frameworks that give the team early warning of degradation in production
Govern LLM usage across CUBE's platform — take ownership of LLM provider relationships (OpenAI, Azure OpenAI, Anthropic), manage API access and versioning, track token consumption, and optimise cost across workloads
Manage the LLM gateway and prompt versioning — maintain tooling such as LangSmith or Helicone for tracing, evaluation, and prompt lifecycle management in production environments
Support experiment tracking and model registry — ensure that experiments are reproducible, models are catalogued, and the path from experiment to production is governed and auditable
Collaborate with data scientists and engineers — work closely with the Lead Data Scientist and Data Engineering team to understand modelling requirements and translate them into reliable production systems
Champion engineering best practices — bring CI/CD, infrastructure as code, and automated testing discipline to ML workflows; reduce toil and manual intervention wherever possible
Drive platform reliability and cost efficiency — monitor infrastructure spend, identify optimisation opportunities, and ensure platform SLAs are met
Interested?
If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE.
CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.