WE'RE HIRING: ML(Machine Learning) Engineer

Location: Vancouver, CA (Onsite)

Duration: 6+ Months

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 cooperative 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

📌 Machine Learning Engineer (Vancouver)
🏢 Experis Canada
📍 Vancouver