Описание: At Aristocrat, we are driving technology in the gaming industry through modern, powerful solutions. Our mission is to bring joy to life through the power of play. We value collaboration, creativity and a strong commitment to excellence. This position will primarily support the Machine Learning area of our Product Madness business unit, a technology company specializing in social casino mobile games. We are looking for a Machine Learning Engineer to join our MLOps team and strengthen our modeling capabilities. You will be responsible for Machine Learning models end to end, from data and experimentation to production deployment. Задачи: Design, train, evaluate and retrain Machine Learning models, with a particular focus on recommendation systems, to support game features, improve the player experience and optimize operational efficiency. Lead the design of model performance monitoring: define which metrics and aspects should be monitored (drift, data quality, performance degradation),



set thresholds and determine what it means for a model to remain reliable from a business perspective. Help establish a strong experimentation culture through model versioning and experiment tracking with tools such as MLflow or Weights & Biases, ensuring that experiments are reproducible and that different model versions can be easily compared and promoted. Contribute to developing a model governance culture: define how models are promoted to production and establish the foundations for communication and collaboration between the MLOps and Data Science teams. Create reusable templates and structures, such as Cookiecutter, to standardize how the Data Science team structures, delivers and communicates its models and experiments. Build robust training and feature engineering pipelines, and develop clean, production-ready code in Python and SQL. Explore and contribute to early-stage projects related to LLMs, RAG and Agentic AI when they add value, as a complement to the role and n

📌 ML engineer in social casino games (España)
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📍 España