ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Aristocrat. Barcelona · full-time.
Pay not stated ML Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and refresh recommender models that support game features, player experience and operational efficiency
- Set production health checks for drift, data quality and model decline, including thresholds tied to business outcomes
- Create repeatable experiment tracking and model promotion practices, and clarify handoffs between MLOps and Data Science
- Develop reusable scaffolding for model handoffs, plus dependable training and feature-engineering pipelines in Python and SQL
- Share model findings, limits and recommendations with product, data and engineering teams
What you bring
- At least four years applying machine learning to practical problems, covering data work through deployment
- You have used recommender systems such as collaborative filtering or ranking in real products
- Strong Python and SQL skills, with experience in ML libraries and large data warehouses
- You have used experiment tracking or model registry tools and contributed to production model monitoring
- A quantitative degree or equivalent experience, plus familiarity with cloud platforms, Docker and Airflow
Who this fits
This mid-level role suits an ML engineer who can take models from experimentation into production and work across technical and business teams. It is hybrid in Barcelona, with three office days each week and minimal travel of up to five percent.
From the employer
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 brighten life through the joy found in play.
Travel Expectations - Minimal travel required (up to 5%)
Hybrid role with 3 days at the office weekly.
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.
This job description may have been reviewed and enhanced using AI-assisted tools to improve clarity, consistency, and inclusivity. All final content, role requirements and hiring decisions remain subject to human review and approval by Aristocrat.
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.