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 Hav** e * 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.