ml engineer for recommender systems in social casino games

Описание:

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. Product Madness specializes in social casino mobile games.

Задачи:

• 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 tracking drift, data quality, and degradation, setting thresholds, and defining model health in business terms.
• Establish experimentation practices, including model versioning and experiment tracking, to make experiments reproducible and models easy to compare and promote.
• Establish model governance practices, define how models are promoted to production, and lay the foundations for communication between MLOps and Data Science.
• Create templates and cookiecutter scaffolding to standardize how Data Science structures, hands off, and communicates models and experiments.
• Build robust training and feature-engineering pipelines and write production-ready Python and SQL.
• Explore and contribute to early-stage projects involving LLMs, RAG, and agentic AI 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 with product, data, and engineering teams, and communicate results, limitations, and recommendations to technical and business audiences.

Требования:

• At least 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.
• Strong analytical foundation, with a background in mathematics, physics, computer science, engineering, or equivalent experience.
• Будет плюсом: Snowflake or similar cloud data warehouses, Generative AI application development and API integration, interest or early experience in RAG systems, familiarity with agentic AI concepts or frameworks, ability to build simple user interfaces such as Streamlit apps.

Условия:

• Minimal travel required (up to 5%).
• Competitive compensation and benefits package; compensation is based on skills, experience, qualifications, and location.
• Depending on role and location, eligibility may include annual bonuses and incentives, health and wellbeing benefits, paid leave, pension plans, insurance coverage, and other local or statutory benefits.

Specific compensation and benefits details will be discussed during the selection process.