Machine Learning Engineer

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. #J-18808-Ljbffr