MLOps Engineer Job Description Template

The MLOps engineer makes machine learning operational: automated pipelines, reproducible training, controlled deployment and monitoring that catches problems before users do. The discipline exists because machine learning systems fail differently from ordinary software, degrading silently as the world changes rather than throwing an error, so the operational practice has to be built specifically for that.

Typical Duties and Responsibilities

• Build automated pipelines covering data, training and deployment
• Implement model versioning, lineage and reproducibility
• Design deployment strategies including shadow, canary and rollback
• Build monitoring for model performance, drift and data quality
• Automate retraining triggered by defined conditions
• Manage environments and dependency consistency across stages
• Implement validation gates that prevent bad models reaching production
• Build alerting for silent model degradation
• Support incident response for model related production issues
• Document operational procedures for each deployed model

Education

• Bachelor’s degree in computer science, engineering or a related field

Required Skills and Experience

• 3+ years in MLOps, DevOps or platform engineering with machine learning exposure
• Strong Python and infrastructure automation skills
• Experience with CI/CD applied to machine learning workflows
• Familiarity with orchestration and pipeline tooling
• Understanding of drift detection and model monitoring
• Containerisation and Kubernetes experience
• Ability to design validation gates that catch real problems
• Good documentation and operational discipline

Preferred Qualifications

• Experience with MLflow, Kubeflow or a managed ML platform
• Exposure to feature stores