> Markdown version of [/jobs/ext/2062480-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2062480-machine-learning-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). --- # Machine Learning Engineer - **Company:** Madrid - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Automation of Tests, Code Review, Continuous Integration, Information Engineering, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Software Engineering, Management of Software Versions, Git, Low Latency, Machine Learning Operations - **Published:** August 15, 2026 - **Apply:** https://www.jobleads.com/es/job/e6b68ee51482d9b07b9ec4df79b154790 ## About the Role * 3+ years of experience developing applied machine learning models * Strong Python skills and good software engineering practices * Experience deploying and operating models in production * Knowledge of classification, anomaly detection, or risk-scoring methods * Experience working with imbalanced datasets and appropriate evaluation metrics * Understanding of precision, recall, false-positive rates, and the business trade-offs between them * Experience with model monitoring, drift detection, versioning, and retraining * Ability to work with large transactional or behavioural datasets * Experience with low-latency inference systems * Confidence working with risk, data engineering, platform, and product teams * Experience using Git, code reviews, automated testing, and CI/CD Previous experience in fraud, payments, insurance, credit risk, or anomaly detection would be valuable, but it is not required if you have worked on comparable production machine learning problems. ## Description * Building and maintaining fraud detection and risk-scoring models used on live transactions * Developing features from transactional, behavioural, account, and device data * Training and evaluating models against new and evolving fraud patterns * Deploying models into low-latency production systems * Reducing false positives while maintaining effective fraud detection rates * Working with risk specialists to translate fraud scenarios and business rules into model features * Designing feedback loops using confirmed fraud cases, manual reviews, and transaction outcomes * Monitoring model performance, feature quality, drift, latency, and prediction distributions * Investigating model degradation and changes in customer or fraud behaviour * Improving model deployment, versioning, retraining, and rollback processes * Documenting model behaviour and decisions for engineers, risk teams, and auditors * Contributing to code reviews, automated testing, CI/CD, and ML engineering standards, * Real-time inference under strict latency constraints, with decisions required before transactions are completed * Highly imbalanced datasets where confirmed fraud represents only a small proportion of all transactions * Fraud patterns that change deliberately in response to existing detection methods * Managing delayed or incomplete labels when transaction outcomes are not immediately known * Balancing fraud detection rates against the commercial and customer impact of false positives * Identifying drift in models and features before it results in significant financial losses * Combining machine learning outputs with business rules and manual risk controls * Meeting explainability and traceability requirements for decisions that may need to be reviewed later * Rolling out new models safely through controlled testing, monitoring, and rollback mechanisms ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Detecting Money Laundering with AI](https://www.wearedevelopers.com/videos/111-detecting-money-laundering-with-ai) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)