Aristocrat

ml engineer in social casino games

In short

ML Engineer role at Aristocrat, a gaming technology company, focused on building and operating recommender systems and ML pipelines for social casino games in the Barcelona office. The role covers model development, experiment tracking (MLflow/W&B), production monitoring, governance, and early-stage LLM/RAG/agentic AI exploration. Requires 4+ years of applied ML experience, strong Python/SQL skills, recommender systems expertise, and cloud/Docker/Airflow experience.

About Aristocrat
Aristocrat develops gaming content and technology, including free-to-play mobile games, regulated land-based gaming, social casino games, and regulated online real-money gaming.
Responsibilities
•Design, train, evaluate, and retrain machine learning models, especially recommender systems, to support game features, player experience, and operational efficiency
•Own model performance monitoring design, including tracked metrics, thresholds, and business-based health criteria
•Establish experimentation practices through model versioning and experiment tracking with tools such as MLflow or Weights & Biases
•Establish model governance practices, define production promotion processes, and lay the foundations for communication between the MLOps team and Data Science
•Create templates and cookiecutter scaffolding to standardise how Data Science structures, hands off, and communicates models and experiments
•Build robust training and feature-engineering pipelines and write clean, production-ready Python and SQL
•Explore and contribute to early-stage LLM, RAG, and agentic AI projects where they add value as a complement to the role
•Develop simple interfaces, such as Streamlit apps, to expose ML capabilities to non-technical users when useful
•Collaborate across product, data, and engineering teams and communicate results, limitations, and recommendations to technical and business audiences
Requirements
•4+ years of experience applying machine learning to real-world problems, from data to deployment
•Proven experience with recommender systems, such as collaborative filtering or ranking, applied to real products
•Strong Python skills and hands-on experience with ML libraries such as scikit-learn, PyTorch/TensorFlow, or XGBoost
•Solid SQL expertise and experience working with large data warehouses
•Hands-on experience with experiment tracking and/or model registry tools such as MLflow or Weights & Biases
•Experience designing or contributing to production model monitoring or performance tracking
•Comfort working with cloud platforms such as GCP, AWS, or Azure, Docker, and Airflow
•A strong analytical foundation and a background in mathematics, physics, computer science, engineering, or equivalent experience
Nice to have
•Snowflake or similar cloud data warehouses
•Generative AI application development, including API integration, RAG systems, agentic AI concepts or frameworks such as LangChain agents or CrewAI, and simple user interfaces such as Streamlit
Conditions
•Minimal travel required (up to 5%)
•Annual bonuses and incentives, health and wellbeing benefits, paid time off, retirement plans, insurance coverage, and other local or statutory benefits may be available depending on role and location
•Specific compensation and benefits details will be discussed during the recruitment process
•Depending on the role, registration with the Nevada Gaming Control Board (NGCB) and/or other gaming jurisdictions may be required

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