Machine Learning Engineer

About the Role

This role sits at the intersection of machine learning, big data engineering, and data science. You will work with large-scale datasets to develop predictive models, recommendation solutions, and intelligent systems that support product performance, user engagement, and business decision-making. You will be involved throughout the ML lifecycle—from data exploration and feature engineering to model development, evaluation, deployment, and continuous optimisation.

Key Responsibilities

• Develop, productionise and maintain machine learning models for recommendation, personalisation, user behaviour, prediction, classification and other data-driven applications.
• Build scalable ML pipelines covering data preparation, feature engineering, model training, evaluation, deployment and monitoring.
• Design and optimise batch and/or real-time model inference solutions for reliability, scalability, latency and production performance.
• Work with large volumes of structured and unstructured data to develop effective machine learning solutions.
• Collaborate closely with Data Scientists and Data Engineers to transform ML prototypes and data pipelines into reliable production systems.
• Monitor model and system performance, identify degradation or operational issues, and continuously improve deployed solutions.
• Contribute to ML engineering practices, including testing, versioning, CI/CD, reproducibility and model lifecycle management.
• Evaluate new developments in machine learning, MLOps and AI and apply relevant technologies to business and product use cases.

Requirements

• Bachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
• 3+ years of relevant experience in building recommendation systems, ranking models, personalisation, or user behaviour modelling.
• Strong programming skills in Python and good software engineering fundamentals.
• Strong understanding of machine learning algorithms, statistics, model evaluation, and feature engineering.
• Experience working with large-scale datasets using Spark, PySpark, Flink or other distributed processing technologies.
• Hands-on experience building or deploying ML models in production environments.
• Strong analytical and problem-solving skills, with the ability to translate business problems into data and machine learning solutions.

Nice to Have

• Experience in gaming, e-commerce, fintech, advertising, or other data-intensive industries.
• Familiarity with cloud platforms such as AWS, Azure, or GCP is an advantage.