ML Ops Engineer

Requirement:

● Hands-on experience building andd eploying ML models to production with at least one of the major hyperscalers, preferably Azure and/or AW
● Experience in managing ML solutions deployed to production
● Strong understanding of ML Ops architecture building blocks, such as Feature Stores and Data pipelines
● Working knowledge of containerization, and Kubernetes is an asset
● Working knowledge of building, automating, and deploying data analytics and Machine Learning (ML) pipelines is an asset
● Experience dealing with internal and external stakeholders is an asset
● Relevant certifications in Azure ML, AWS SageMaker, Databricks and/or GCP are an asset