Machine Learning Engineer (MLOps)

As an MLOps Engineer, you industrialize Machine Learning. You build the bridge between Data Science and Operations by creating robust, automated pipelines that accelerate the lifecycle of ML models.

Your Responsibilities

• Conception and implementation of CI/CD/CT pipelines (Continuous Integration/Deployment/Training) for machine learning models.
• Automation of the entire ML lifecycle, from data preprocessing to model training to deployment and monitoring.
• Building and managing scalable infrastructure for ML workloads on Kubernetes and cloud services like AWS SageMaker.
• Implementation of comprehensive monitoring to oversee model drift, performance, and data quality.
• Close partnership with Data Scientists to make their models production-ready and establish best practices.

Your Profile

• You are an experienced software developer with sound knowledge of Python and its Data Science ecosystem.
• You possess a solid understanding of the entire Machine Learning lifecycle.
• You have practical experience with Docker, Kubernetes, and modern CI/CD tools (e.g., GitHub Actions, Jenkins).
• You are familiar with MLOps tools and frameworks like Kubeflow, MLflow, or comparable technologies.
• Experience with a major cloud platform (preferably AWS) and its AI/ML services is a decisive advantage.

What We Offer