MLE Professional

• Design and implement effective feature engineering using Azure tools.
• Set up and manage a feature store on Azure for centralized feature storage.
• Develop machine learning models using Azure ML, incorporating engineered features.
• Deploy models on Azure, ensuring seamless integration with applications.
• Establish monitoring systems for model performance and conduct regular maintenance.
• Integrate ML solutions with Azure services like Databricks and SQL Database.

Remote is also fine

• Good in python SQL, API written and packaged deploy, some knowledge of ML, good to have knowledge on CI CD pipeline Min 5+ years

Required Skills:

• Azure ML and Data Services: Proficient in Azure ML and relevant data services.
• Feature Store Management: Experience setting up and managing feature stores on Azure.
• Feature Engineering Techniques: Strong understanding and application of feature engineering.
• Programming (Python): Proficient in Python and ML frameworks .
• Model Deployment: Ability to deploy models as APIs on Azure.
• Monitoring and Optimization: Skill in implementing monitoring solutions and optimizing models.
• Collaboration and Communication: Effective collaboration with cross-functional teams.