Senior AI/ML Ops Engineer
As a Senior AI/ML Ops Engineer, you will join the Data Enlightenment team (Data Scientists, Software Engineers, Product Managers) working on a large-scale AI/ML project. The mission of the team is to build a strong data and AI foundation that enables innovative, ML-driven capabilities across both B2C (app/website) and B2B (management tools) product lines.
Your role will focus on building and evolving the AI/ML platform, ensuring scalable, reliable, and efficient deployment and monitoring of machine learning and GenAI solutions. You will collaborate closely with Data Scientists, Software Engineers, Architects, Data Engineers, and Data Ops to ensure technical alignment and consistency across components.
Why You Will Love This Project
- Work on a project that is transforming digital experiences on a global scale
- Combine AI/ML innovation with real product impact
- Collaborate with a diverse and expert team across data, engineering, and product
- Shape the long-term vision and architecture of the AI/ML platform
Responsibilities
- Build and maintain production-grade AI/ML pipelines, including CI/CD processes for training, validating, deploying, monitoring, and retraining models
- Transition ML and GenAI prototypes into scalable and reliable production systems
- Optimize and monitor AI/ML systems for performance, reliability, cost-efficiency, and traceability
- Define and implement MLOps best practices such as model lifecycle management, data lineage, versioning, automated testing, governance, and alerting
- Design and deploy pipelines for real-time inference, batch processing, and automated retraining
- Implement monitoring systems to detect model drift and performance issues
- Integrate advanced AI/ML capabilities, including vector search, embeddings-based similarity services, and LLM-based applications
- Collaborate with product, engineering, and data teams to align ML infrastructure with technical and business goals
- Contribute to the evolution of the AI/ML platform, including model registries and orchestration tools
- Build dashboards to track platform efficiency and ensure cost-effective operations
- Advocate for and implement Python development standards and reusable ML pipelines across teams
- Develop and maintain internal Python libraries to streamline workflows
Requirements
- 6+ years of experience in ML Engineering, MLOps or DevOps for AI/ML systems (or 3+ years with a PhD)
- Expertise in MLOps principles and tools (testing, deployment, monitoring, versioning)
- Proven experience building production-grade ML systems in Python
- Hands-on experience with cloud platforms (AWS preferred) and container orchestration (Docker, Kubernetes)
- Experience with ML platforms such as MLflow, SageMaker, Vertex AI or similar
- Strong understanding of the complete ML lifecycle, including training, validation, retraining, A/B testing and drift detection
- Experience with CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins) and Infrastructure as Code (Terraform, CloudFormation)
- Proficiency in Python and experience with scalable data processing frameworks (Spark, Airflow, Kubeflow)
- Familiarity with monitoring/logging tools like Prometheus, Grafana and Datadog
- Professional English proficiency
- Ability to work from Paris, Milan, Turin, Barcelona or remotely within a compatible time zone
Nice to have
- Experience deploying or scaling GenAI solutions
- Contributions to shared internal libraries or developer tools
- Additional language skills (French, Spanish, Italian)
- Familiarity with large-scale distributed systems or marketplace environments
