AI Engineer

Join to apply for the AI Engineer role at Cube. CUBE is 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 are a fast‑paced, high‑performing team that thrives on pushing boundaries and 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. Role Overview As a Machine Learning Engineer, you will drive the development and continuous improvement of our AI/ML‑powered products, including recommender systems, NLP solutions, and LLM‑driven features. Your mission is to deliver advanced, production‑ready ML solutions that delight users while maintaining excellence in cloud‑based ML engineering with equal focus on Development and Operations. Key Responsibilities Develop, improve and productionise ML and LLM‑based features for our internal and external platforms to solve challenges like: High cardinality hierarchical classifications Personalised content filtering of legal documents Using ML/LLMs to network heterogeneous text data at scale Building agent‑ready tools to leverage insights from our knowledge graph Leverage cloud architecture (primarily Azure) to deploy interpretable ML/DL solutions that scale efficiently. Collaborate with SMEs to ground research proposals to CUBE\’s dynamic, unstructured, high‑dimensional data. Continuously seek opportunities to innovate, learn, and apply the latest research to new products and services. Requirements Experience