> Markdown version of [/jobs/ext/3695170-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3695170-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Ml Engineer

- **Company:** Aristocrat Technologies
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Monitoring of Systems, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Standard Sql, Google Cloud, Pytorch, Retrieval-Augmented Generation, Large Language Models, Agentic-AI, Scikit Learn, Xgboost, Machine Learning Operations, Drift Detection, Streamlit Framework, Software Version Control, Docker
- **Published:** October 12, 2026
- **Apply:** https://www.buscojobs.com.es/ml-engineer-en-barcelona-ID-374204703

## About the Role

experience from data to deployment * Proven experience with recommender systems applied to real products * Strong Python skills and ML libraries (scikit-learn, PyTorch/TensorFlow, XGBoost) * Solid SQL expertise and experience with large data warehouses * Hands-on experience with experiment tracking/model registry tools (MLflow, Weights u**** Biases) * Experience designing or contributing to model monitoring in production * Cloud familiarity (GCP, AWS, Azure), Docker, and Airflow * Quantitative background (math, physics, CS, engineering) or equivalent Requisitos principales * health and wellbeing benefits * paid time off * retirement plans * insurance coverage * annual bonuses and incentives * local or statutory benefits

## Description

Experteer Overview As an ML Engineer in the Product Madness MLOps team, you will own end-to-end ML models-from data and experimentation to production, with a focus on recommender systems that enhance gameplay and operational efficiency.You'll shape model monitoring, experiment tracking, and governance, ensuring models stay reliable and aligned with business goals.You'll collaborate closely across product, data, and engineering teams and contribute to innovative AI initiatives, including MLOps tooling and lightweight interfaces for non-technical users.Compensaciones / Beneficios * Design, train, evaluate and retrain ML models, emphasizing recommender systems for game features and player experience * Define and implement model performance monitoring, including drift detection, data quality and health thresholds * Establish and promote an experimentation culture with model versioning and experiment tracking (MLflow, Weights u**** Biases) * Support model governance, including production promotion processes and communication contracts between MLOps and Data Science * Create templates and scaffolding to standardize DS workflows, models, and experiments * Build robust training and feature pipelines; write production-ready Python and SQL * Explore early-stage projects involving LLMs, RAG, and agentic AI as value-adds * Develop simple interfaces (e.g., Streamlit) to expose ML capabilities to non-technical users * Collaborate with cross-functional teams and clearly communicate results and limitations Responsabilidades * 4+ years of ML experience from data to deployment * Proven experience with recommender systems applied to real products * Strong Python skills and ML libraries (scikit-learn, PyTorch/TensorFlow, XGBoost) * Solid SQL expertise and experience with large data warehouses * Hands-on experience with experiment tracking/model registry tools (MLflow, Weights u**** Biases) * Experience designing or contributing to model monitoring in production * Cloud familiarity (GCP, AWS, Azure), Docker, and Airflow * Quantitative background (math, physics, CS, engineering) or equivalent Requisitos principales * health and wellbeing benefits * paid time off * retirement plans * insurance coverage * annual bonuses and incentives * local or statutory benefits