Описание:

Aristocrat develops content and technology for the gaming industry and publishes free-to-play mobile games. Its business units cover regulated land-based gaming, social casino, and regulated real-money online gaming.

Задачи:

Design, train, evaluate, and retrain Machine Learning models, especially recommendation systems, to support game features, improve player experience, and optimize operational efficiency; Lead the design of model performance monitoring by defining metrics and aspects to track, setting thresholds, and determining model reliability from a business perspective; Establish experimentation practices through model versioning and experiment tracking with tools such as MLflow or Weights & Biases, ensuring reproducibility and easy comparison and promotion of model versions; Contribute to model governance practices by defining how models are promoted to production and establishing communication and collaboration processes between MLOps and Data Science teams; Create reusable templates and structures, such as Cookiecutter, to standardize how the Data Science team structures, delivers, and communicates models and experiments; Build robust training and feature engineering pipelines and develop clean, production-ready Python and SQL code; Explore and contribute to early-stage projects involving LLMs, RAG, and Agentic AI when they add value; these are complementary to the role, not its primary responsibility; Develop simple interfaces, such as with Streamlit, to make Machine Learning capabilities accessible to non-technical users when useful; Collaborate with Product, Data, and Engineering teams, clearly communicating results, limitations, and recommendations to technical and business audiences.

Требования:

More than 4 years of experience applying Machine Learning to real-world problems,



from data processing through model deployment; Demonstrated experience with recommendation systems, such as collaborative filtering, ranking, or similar techniques, applied to real products; Strong Python skills and practical experience with Machine Learning libraries such as scikit-learn, PyTorch/TensorFlow, XGBoost, or similar; Strong SQL skills and experience working with large data warehouses; Practical experience with experiment-tracking tools and/or model registries such as MLflow, Weights & Biases, or equivalent; Experience designing or contributing to systems for monitoring and tracking model performance in production; Experience with cloud platforms such as GCP, AWS, or Azure, as well as Docker and Airflow; A strong analytical foundation, with education in a quantitative field such as mathematics, physics, computer science, or engineering, or equivalent professional experience; Nice to have: experience with Snowflake or similar cloud data warehouses, practical experience developing generative AI applications (LLMs), including API integration, interest or early experience with RAG systems through prototypes, personal projects, or professional experience, interest or familiarity with Agentic AI concepts or frameworks such as LangChain Agents or CrewAI, ability to develop simple user interfaces such as Streamlit to make Machine Learning models accessible to other teams.

Условия:

Travel availability of up to 5% is required; The compensation package may include annual bonuses and incentives, health and wellness benefits, paid time off, retirement plans, insurance coverage, and other local or legally mandated benefits, depending on the role and location; Compensation and benefits details will be discussed during the selection process; The role may require registration with the Nevada Gaming Control Board (NGCB) and/or other regulators in gaming jurisdictions where the company operates.

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📌 ml engineer for social casino games (Barcelona)
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