ML Engineer: Recommender Systems & MLOps (Onsite)

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

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.