About the Company

A long-established, globally regulated fintech and trading group operating across the UAE, UK, Europe, Asia, and Australia is expanding its data and engineering capabilities. The company builds advanced trading products across CFDs, crypto, and multi-asset platforms, and is investing heavily in AI, machine learning, and next-generation data infrastructure to enhance trader experience, improve personalisation, and drive growth.

We are looking for an AI Data Engineer to join the team and help transform complex, high-volume datasets into intelligent systems that power personalisation, predictive modelling, and GenAI-driven applications for millions of users worldwide.

Key Responsibilities

• Partner with marketing, product, and data teams to build meaningful datasets and a centralised data warehouse used for reporting, customer insights, and LTV-focused decision-making.
• Develop and own end-to-end machine learning solutions including predictive modelling, churn prediction, and customer segmentation.
• Build AI-driven personalisation systems that improve customer engagement and power multi-channel marketing strategies.
• Explore and deploy GenAI applications that summarise large datasets, enhance trader workflows, and create personalised trading experiences.
• Design and maintain data pipelines and automations connecting multiple APIs and data sources.
• Identify new opportunities where data, AI, and automation can increase customer engagement or streamline internal operations.

Requirements

• Strong experience with SQL and working with raw, high-volume datasets such as customer journeys, marketing performance, and product behaviour data.
• Proficiency in Python, with experience building pipelines and API integrations across various platforms and tools.
• Hands-on expertise in deploying machine learning solutions end-to-end, particularly for predictive modelling and segmentation.
• Experience uncovering customer insights, communicating them to stakeholders, and shaping engagement or LTV-focused strategies.
• Ability to build LLM-powered applications that summarise or analyse large data volumes, with awareness of the latest GenAI use cases.
• Cloud computing experience, ideally AWS.
• Experience with ETL tooling such as dbt or Informatica.