At Aristocrat, we are advancing gaming technology with modern, powerful solutions. These roles primarily support the Machine Learning area of our Product Madness business line — a tech company specialised in social casino mobile games. Design, train, evaluate and retrain machine learning models with emphasis on recommender systems — that support game features, player experience, and operational efficiency.

Own the design of our model performance monitoring: define what to track (drift, data quality, degradation), thresholds, and what "the model is still healthy" means in business terms.

Help establish a model governance culture: define how models are promoted to production and lay the foundations of the communication contract between the MLOps team and Data Science. Create templates and cookiecutter scaffolding to standardise how Data Science structures, hands off, and communicates its models and experiments. Build robust training and feature-engineering pipelines, and write clean, production-ready Python and SQL.

Explore and contribute to early-stage projects involving LLMs, RAG and agentic AI where they bring value — as a complement to the role, not its core. Collaborate with teams across product, data, and engineering, and communicate results, limitations,



and recommendations clearly across technical and business audiences. ??????????: 4+ years of experience applying machine learning to real-world problems, from data to deployment.

Strong

Python skills and hands-on experience with ML libraries (scikit-learn, PyTorch/TensorFlow, XGBoost or similar). Solid SQL expertise and experience working with large data warehouses.

Experience designing or contributing to model monitoring / performance tracking in production. Comfort working with cloud platforms (e.g., GCP, AWS, Azure), Docker, and Airflow. A strong analytical foundation, with a background in a quantitative field (mathematics, physics, computer science, engineering) or equivalent experience. ????? ??????: Experience with Snowflake or similar cloud data warehouses; ????? ??????: Interest or early experience in RAG systems — whether through prototypes, side projects, or professional work; ????? ??????: Hybrid role with 3 days at the office weekly.

Depending on your role and location, you may be eligible for annual bonuses and incentives, health and wellbeing benefits, paid time off, retirement plans, insurance coverage, and other local or statutory benefits. #

📌 Ml Engineer In Recommender Systems (Barcelona)
🏢 Aristocrat
📍 Barcelona