MLops Engineer

Role overview Build and operate the infrastructure that brings machine learning models into production for contextual advertising across the open web and connected TV. The role covers model delivery, data and training pipelines, observability, and efficiency, with close collaboration across data, platform, and backend engineering.

Responsibilities - Deploy, maintain, and optimize production ML models for reliability, performance, and low latency. - Build pipelines for dataset generation, model training, validation, and performance analysis. - Create monitoring, dashboards, drift detection, and alerts to track model quality and stability. - Improve CI/CD, model versioning, reproducibility, and infrastructure automation. - Find ways to reduce model serving costs while maintaining model quality. - Develop internal workflow automations and integrations, and help troubleshoot production systems with partner teams.

Requirements - Practical experience developing ML models and deploying them in production. - Knowledge of MLOps practices, including CI/CD, versioning, reproducibility, and monitoring. - Experience with ML observability, performance metrics, dashboards, and alerting. - Understanding of distributed systems, microservices, Kubernetes, and cloud infrastructure. - Strong troubleshooting skills across models, data pipelines, APIs, and infrastructure. - Backend engineering skills that include APIs, asynchronous programming, testing, and clean architecture; ability to take ownership and work autonomously.

Nice to have - Experience with AI workflow automation or agentic integrations.

Benefits and work setup - Remote or hybrid options; remote contracts are available in Spain, Italy, the UK, Belgium, the Netherlands, France, and Germany. - Learning platform, optional language classes, team activities, and offsites. - Home office setup budget up to €1,000, paid trips to the Madrid headquarters, and a MacBook Pro M3.