Machine Learning Engineer

About the RoleThis 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 ResponsibilitiesDevelop and productionise machine learning models for user behaviour, recommendations, prediction, classification, and other data-driven applications.Analyse large and complex datasets to identify patterns, trends, and opportunities for product and business improvement.Perform data exploration, feature engineering, model selection, training, validation, and performance evaluation.Build scalable data and ML solutions to process large volumes of structured and unstructured data.Collaborate with Data Engineers to prepare, transform, and optimise datasets for machine learning applications.Optimise models for accuracy, scalability, latency, and production performance.Stay up to date with developments in machine learning, big data, and MLOps and evaluate their applicability to the company's products.RequirementsBachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.2+ years of experience in Machine Learning, Data Science or ML Engineering.Strong programming skills in Python and experience with common ML/data science libraries such as scikit-learn, Pandas, NumPy, PyTorch, or TensorFlow.Strong understanding of machine learning algorithms, statistics, model evaluation, and feature engineering.Experience working with large-scale datasets and distributed data processing technologies such as Spark, PySpark, Flink, or similar.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 HaveFamiliarity with cloud platforms such as AWS, Azure, or GCP is an advantage.Experience with recommendation systems, ranking models, personalisation, or user behaviour modelling.Experience in gaming, e-commerce, fintech, advertising, or other data-intensive industries.Familiarity with ML deployment technologies such as Docker, Kubernetes, MLflow, or Kubeflow.