ML Engineer

Aristocrat

. Design, train, evaluate, and retrain machine learning models, especially recommender systems .

Posted 9/22/2026full-timeBarcelona • SpainMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, training, and evaluating machine learning models, particularly recommender systems, while ensuring model performance monitoring and governance practices are established. Proficient in Python, SQL, and ML libraries, with a strong analytical foundation and experience in cloud platforms.

Highest-signal resume keywords

Machine Learning Model DevelopmentRecommender Systems ExpertisePython ProgrammingMLflow or Weights & Biases ExperienceModel Monitoring and Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningRecommender SystemsPythonSQLML LibrariesExperiment TrackingModel MonitoringCloud PlatformsDockerAirflow

Soft Skills

Analytical SkillsCommunication

Tools & Technologies

MLflowWeights & BiasesGCPAWSAzureDockerAirflow

Certifications & Qualifications

Nevada Gaming Control Board Registration

Industry Keywords

Data ScienceMLOpsModel GovernanceProduction DeploymentLarge Data Warehouses

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDockerGoogle Cloud PlatformPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, train, evaluate, and retrain machine learning models, especially recommender systems
Support game features, player experience, and operational efficiency
Own model performance monitoring, including drift, data quality, degradation, thresholds, and business health criteria
Establish experimentation practices using model versioning and experiment tracking tools such as MLflow or Weights & Biases
Establish model governance practices for production promotion and communication between MLOps and Data Science
Create templates and cookiecutter scaffolding to standardise model and experiment structures, handoffs, and communication
Build training and feature-engineering pipelines
Write clean, production-ready Python and SQL
Explore early-stage LLM, RAG, and agentic AI projects where valuable
Develop simple interfaces such as Streamlit applications for non-technical users
Collaborate with product, data, and engineering teams
Communicate results, limitations, and recommendations to technical and business audiences

Requirements

What you’ll need

4+ years of experience applying machine learning to real-world problems, from data to deployment
Proven experience with recommender systems applied to real products
Strong Python skills
Hands-on experience with ML libraries such as scikit-learn, PyTorch/TensorFlow, and XGBoost or similar
Solid SQL expertise
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 model monitoring/performance tracking in production
Comfort working with cloud platforms such as GCP, AWS, or Azure
Experience with Docker and Airflow
Strong analytical foundation
Background in mathematics, physics, computer science, engineering, or equivalent experience
Ability to work with model versioning, experiment tracking, model governance, and production deployment practices
Depending on the nature of the role, may be required to register with the Nevada Gaming Control Board and/or other gaming jurisdictions

Benefits

Comp & perks

Annual bonuses and incentives may be available depending on role and location
Health and wellbeing benefits may be available depending on role and location
Paid time off may be available depending on role and location
Retirement plans may be available depending on role and location
Insurance coverage may be available depending on role and location
Other local or statutory benefits may be available depending on role and location
Comprehensive pay and benefits package
Minimal travel required (up to 5%)