Machine Learning Engineer en España

• Solid experience as an ML Engineer / MLOps Engineer.

• Experience industrializing and deploying Machine Learning models.

• Advanced experience in GCP.

• Vertex AI / Vertex AI Pipelines.

• Airflow.

• Experience developing productive Python.

• Software Engineering best practices.

• Testing and version control.

• Experience optimizing pipelines.

• Experience with large volumes of data and storage optimization.

• Control-M.

• Experience in monitoring models in production.

• Knowledge of data drift / model monitoring.

• Real experience working with productive models.

• Ability to resolve incidents in production environments.

📍 Location: Spain
🏢 Modality: Remote
💼 Position: ML Engineer
🧩 Experience: Senior
☁️ Environment: Google Cloud Platform
🚀 Specialization: MLOps / Model industrialization

We are looking for an ML Engineer to industrialize, deploy, and maintain in production the Machine Learning models developed by the Data Science teams.

The person will be responsible for converting analytical developments into productive, scalable, efficient, and monitored solutions, applying MLOps and Software Engineering best practices.

• Industrialize Machine Learning models for production deployment.

• Deploy models as batch processes or services on GCP.

• Work with Vertex AI Pipelines and Airflow.

• Design solutions with high availability and scalability.

• Transform code developed by Data Scientists into productive, efficient, and maintainable code.

• Apply software engineering best practices:

  • Testing.

  • Version control.

  • Modularity.

  • Code quality.

• Optimize Machine Learning pipelines.

• Optimize data storage and query processes.

• Work with techniques such as:

  • Partitioning.

  • Materialized views.

  • Deltas.

  • Storage optimization.

• Build and maintain complex and automated workflows.

• Use Airflow and Control-M for process scheduling and execution.

• Implement monitoring and alerting mechanisms.

• Detect data drift and model performance degradation.

• Optimize cloud resource consumption.

• Maintain and support models deployed in production.

• Directly handle 2N incidents for assigned projects.