ML Engineer

Responsabilities :

Enhance end-to-end machine learning solutions developed and deployed, from data pre-processing and feature engineering to model training, evaluation, deployment and monitoring.
Enhance and implement templates/recipes to boost the scalability of AI use cases.
Refactor notebooks into high quality code scripts that will be part of the ML pipelines.
Design and optimize ML pipelines, leveraging its features and capabilities to streamline the model development and deployment process.
Build and maintain scalable and reliable MLOps infrastructure, facilitating model versioning, monitoring, and automated deployment.
Codify best practices for model performance monitoring, model drift detection, and model retraining, ensuring high-quality and up-to-date ML models in production.
Implement repeatable and reliable CI/CD pipelines.
Implement data, model and code tests.
Stay up to date with the latest advancements in MLOps, LLMOps , and proactively identify opportunities to enhance our ML capabilities.
Mentor and provide technical guidance to the Data Science teams across entities.
Build assets and guidelines with our internal MLOps community.

Technical skills :

Azure (DevOps, Services, SQL, Azure Search)
Machine Learning (mlflow, Databricks, Kubeflow)
Python