MLOps Engineer
virtual vocationsDenver (CO)
About the role
To support a growing AI/ML infrastructure, the full-time remote MLOps Engineer will design, provision, and maintain the Azure environment while building end-to-end ML pipelines and ensuring security compliance.
Key responsibilities:
Design and provision Azure resource groups, networking, and identity management for AI/ML workloads
Build and implement end-to-end ML pipelines, including model training, evaluation, and deployment workflows
Monitor Azure consumption and set budgets to optimize spending against the approved AI CoE budget
Required qualifications:
Experience with Azure Machine Learning and Azure resource management
Proficiency in building ML pipelines using Azure ML Pipelines or Fabric Data Factory
Knowledge of data encryption, security compliance, and IT governance frameworks
Familiarity with Power BI and semantic model creation for analytics
Experience in developing PySpark and Python notebooks for data analysis and feature engineering
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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