ML Ops Engineer

Paris, FranceFull-TimeMid-levelAI / Data Science

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Design, implement and maintain ML pipelines and CI/CD workflows (training, deployment, retraining)
Expose ML models through inference APIs (Cloud Run / Vertex AI Endpoints) to be consumed by product and platform services
Own monitoring, alerting and reliability of ML systems in production
Write optimized, high-performance, scalable, testable, maintainable, and observable code
Ensure operational readiness of ML models (logging, metrics, incident handling)
Act as a local ML Ops reference for the Paris Data Science team
Support DS teams to industrialize models and experiments

Work in an international, multi-brand environment with teams across Europe, Canada, and the US
Collaborate with platform teams (Canada/US) on GCP infrastructure and standards
Work closely with Product squads of E&E brands to understand and address their needs
Collaborate with Data Engineers and software architect to ensure data quality and production deployment of models
Promote a data-driven culture in a highly collaborative environment and share your expertise

Stay up to date with advances in machine learning and devops to apply best practices within the company

You hold an engineering degree or a Master’s degree in mathematics or computer science, with at least 5–6 years of experience in similar roles
You enjoy discovering scientific methods and challenging existing ones, always keeping in mind the ultimate goal of improving the performance of our dating application
Experience with ML / DevOps practices
Experience with CI/CD, Docker, Terraform
Experience with monitoring & alerting (Cloud Monitoring, Grafana, etc.)
Understanding of IAM, service accounts, security
Comfortable operating ML systems in production
Strong understanding of model serving constraints
Comfortable communicating in English during meetings
Excellent analytical and synthesis skills
Excellent written and verbal communication skills
Rigorous scientific mindset
Team spirit and proactive attitude
Curiosity and strong learning ability

Experience working with Kafka or event-driven architectures in ML or data platforms
Knowledge in A/B test framework
Knowledge in QlikView, Looker, or other BI tools