Senior Machine Learning Engineer

Prezzee is a global leader in digital gifting and payments, having sold over 140 million gift cards through partnerships with more than 1,500 brands and serving over 6 million customers worldwide. Our mission is to change lives through the power of giving — leveraging borderless payment technology and innovative digital solutions to help people connect and create memorable moments that truly matter.

• →Design & Innovate: Create and refine machine learning algorithms that enhance customer experience (CX) and fight fraud, pushing the boundaries of what’s possible.
• →Optimise Performance: Experiment with new techniques, architectures, and feature selection to ensure top-tier model performance.
• →Clean and Curate Data: Work with large datasets, ensuring high-quality data for training and testing – setting the stage for powerful, reliable models.
• →Deploy & Scale: Deploy and maintain machine learning models in production environments, ensuring that they are scalable, efficient, and reliable.
• →Collaborate Cross-Functionally: Collaborate closely with fraud analysts, software engineers, product managers, and business stakeholders to ensure our ML solutions align with the company’s goals, particularly as it relates to fraud detection and prevention.
• →Stay at the Forefront of Innovation: Always be learning and experimenting with emerging trends in machine learning and AI to keep us ahead of the curve.

• Strong background in probability, statistics and algorithms, with the ability to apply these to fraud detection and prevention.

• Expertise in machine learning frameworks such as Keras and PyTorch.

• Experience deploying machine learning models on cloud platforms such as AWS, GCP or Azure.

• Experience building scalable machine learning systems and deploying models in real time.

• Familiarity with data structures, data modelling and software architecture.

• Experience with fraud detection techniques, including anomaly detection and graph-based analytics.

• Solid understanding of MLOps practices and CI/CD pipelines for machine learning models.

• Strong hands-on experience with Databricks, including developing and maintaining Delta Live Tables, troubleshooting production data pipelines, and optimising compute performance and cost.