ML Engineer at Aristocrat

Aristocrat; Barcelona; Presencial; EUR 55.000 - 85.000; Jornada completa

Descripción de la vacante

Aristocrat is seeking an ML Engineer to join the MLOps team and own machine learning models end to end, from data and experimentation to production, in our Product Madness unit in Barcelona. You will design, train, and monitor models for recommender-like features in social casino mobile games.

The role emphasizes building robust pipelines, experiment tracking, and collaboration with cross-functional teams across product and data science to deliver business value.

Formación

• 4+ years of experience applying machine learning to real-world problems.
• Proven experience with recommender systems applied to real products.
• Strong Python skills and hands-on experience with ML libraries (scikit-learn, PyTorch/TensorFlow, XGBoost).
• Solid SQL expertise and experience working with large data warehouses.
• Hands-on experience with experiment tracking and/or model registry tools (MLflow, Weights & Biases, or equivalent).
• 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 (math, physics, CS, engineering) or equivalent experience.
• Nice to Have Experience with Snowflake or similar cloud data warehouses.
• Hands-on experience building applications with Generative AI (LLMs), including API integration.
• Interest or early experience in RAG systems.
• Interest or familiarity with agentic AI concepts or frameworks (e.g., LangChain agents, CrewAI).
• Ability to build simple user interfaces (e.g., Streamlit) to expose ML models to wider teams.

Responsabilidades

• 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 model performance monitoring: define what to track, thresholds, and what the model is still healthy means in business terms.
• Help establish a solid experimentation culture: model versioning and experiment tracking with tools such as MLflow or Weights & Biases.
• Help establish a model governance culture: define how models are promoted to production and communication contract between MLOps and Data Science.
• Create templates and cookiecutter scaffolding to standardise DS structure and handoffs.
• Build robust training and feature-engineering pipelines, and write production-ready Python and SQL.
• Explore early-stage projects involving LLMs, RAG and agentic AI for value addition.
• Develop simple interfaces (e.g., Streamlit) to expose ML capabilities to non-technical users.
• Collaborate with product, data, and engineering teams, communicating results and recommendations clearly.

Conocimientos

Recommender systems

Python

SQL

Experiment tracking

Model monitoring

Cloud platforms

Docker

Airflow

LLMs / Generative AI

Streamlit UI

Herramientas

MLflow

Weights & Biases

Docker

Airflow

Streamlit

Descripción del empleo

This Full time on site position offers great opportunities for career growth. At Aristocrat, we are advancing gaming technology with modern, powerful solutions. Our mission is to bring happiness to life through play. We value collaboration, creativity, and a strong passion for excellence. These roles primarily support the Machine Learning area of our Product Madness business line — a tech company specialised in social casino mobile games. We are looking for an ML Engineer to join our MLOps team and strengthen its modeling core. You will own machine learning models end to end, from data and experimentation to production, and help us build the monitoring and experimentation culture that keeps them reliable and aligned with the business over time. You will make a real difference in our products and players' experiences.

What You'll Do

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 solid experimentation culture: model versioning and experiment tracking with tools such as MLflow or Weights \& Biases, so experiments are reproducible and model versions are easy to compare and promote. 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. Develop simple interfaces (e.g., Streamlit) to expose ML capabilities to non-technical users when useful. Collaborate with teams across product, data, and engineering, and communicate results, limitations, and recommendations clearly across technical and business audiences.

What We're Looking For

• 4+ years of experience applying machine learning to real-world problems, from data to deployment.
• Proven experience with recommender systems (collaborative filtering, ranking, or similar) applied to real products.
• 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.
• Hands-on experience with experiment tracking and/or model registry tools (MLflow, Weights \& Biases, or equivalent).
• 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.
• Nice to Have Experience with Snowflake or similar cloud data warehouses.
• Hands-on experience building applications with Generative AI (LLMs), including API integration.
• Interest or early experience in RAG systems — whether through prototypes, side projects, or professional work.
• Interest or familiarity with agentic AI concepts or frameworks (e.g., LangChain agents, CrewAI).
• Ability to build simple user interfaces (e.g., Streamlit) to expose ML models to wider teams.

Why Aristocrat?

Aristocrat is a world leader in gaming content and technology, and a top-tier publisher of free-to-play mobile games. Aristocrat has three operating business units, spanning regulated land-based gaming (Aristocrat Gaming), social casino (Product Madness), and regulated online real-money gaming (Aristocrat Interactive). Our team of over 7,300 employees worldwide is united by our company’s mission to bring joy to life through the power of play. We deliver great performance for our B2B customers and bring joy to the lives of the millions of people who love to play our casino and mobile games. And while we focus on fun, we never forget our responsibilities. We strive to lead the way in responsible gameplay, and to lift the bar in company governance, employee wellbeing and sustainability. We’re a diverse business united by shared values and an inspiring mission to bring joy to life through the power of play.

Travel Expectations

Minimal travel required (up to 5%)

Additional Information

Depending on the nature of your role, you may be required to register with the Nevada Gaming Control Board (NGCB) and/or other gaming jurisdictions in which we operate.

Compensation Philosophy

• We offer a comprehensive pay and benefits package designed to stay competitive in the market, support your wellbeing, and recognise your contribution to our success.
• Our approach is underpinned by a pay-for-performance belief in rewarding individual impact.
• Your specific compensation package will be determined by factors such as your skills, experience, qualifications, and location.
• 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.
• Specific details about compensation and benefits for this position will be discussed during the recruitment process.