ML Engineer

We are looking for an ML engineer with expertise in Unity Catalog and Feature Store in Databricks to help us build and maintain a solid foundation for our data and machine learning workflows. You will work on organizing data, managing access, and enabling machine learning models to operate efficiently in production

What You Will Do -

• Set up and manage Unity Catalog in Databricks to organize and secure data access across teams
• Design and operationalize Feature Stores to support machine learning models in production
• Build efficient data pipelines to process and serve features to ML workflows
• Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions
• Monitor and optimize the performance of pipelines and feature stores

What We are Looking For -

• Strong experience with Unity Catalog in Databricks for managing data assets and access control
• Hands-on experience working with Databricks Feature Store or similar solutions
• Knowledge of building and maintaining scalable ETL pipelines in Databricks
• Familiarity with Azure tools like Azure Cosmos DB and ACR
• Understanding of machine learning workflows and how feature stores fit into the pipeline
• Strong problem-solving skills and a collaborative mindset
• Proficiency with Java
• Proficiency in Python and Spark for data engineering tasks
• Experience with monitoring tools like Splunk or Datadog to ensure system reliability
• Familiarity with AKS for deploying and managing containers

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

We are looking for an ML engineer with expertise in Unity Catalog and Feature Store in Databricks to help us build and maintain a solid foundation for our data and machine learning workflows. You will work on organizing data, managing access, and enabling machine learning models to operate efficiently in production

What You Will Do -

• Set up and manage Unity Catalog in Databricks to organize and secure data access across teams
• Design and operationalize Feature Stores to support machine learning models in production
• Build efficient data pipelines to process and serve features to ML workflows
• Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions
• Monitor and optimize the performance of pipelines and feature stores

What We are Looking For -

• Strong experience with Unity Catalog in Databricks for managing data assets and access control
• Hands-on experience working with Databricks Feature Store or similar solutions
• Knowledge of building and maintaining scalable ETL pipelines in Databricks
• Familiarity with Azure tools like Azure Cosmos DB and ACR
• Understanding of machine learning workflows and how feature stores fit into the pipeline
• Strong problem-solving skills and a collaborative mindset
• Proficiency with Java
• Proficiency in Python and Spark for data engineering tasks
• Experience with monitoring tools like Splunk or Datadog to ensure system reliability
• Familiarity with AKS for deploying and managing containers

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.