Review and refine AI-generated content related to MLOps workflows, machine learning pipelines, automation, monitoring, and deployment.
Evaluate outputs for technical validity, reproducibility, and industry best practices in MLOps.
Draft realistic scenarios covering pipeline orchestration, CI/CD for machine learning, model serving, monitoring, drift detection, and scaling infrastructure.
Assess AI reasoning in topics such as containerization, cloud platform deployment, data versioning, experiment tracking, and model lifecycle management.
Identify gaps or inaccuracies in approaches to operationalizing machine learning.
Create scenario variations from the perspective of different MLOps stakeholders: data scientists, engineers, DevOps, and business leaders.

Requirements

An MLOps engineer, ML platform developer, or machine learning operations expert
Based in the EU or UK
With several years of experience in machine learning operations, ML pipelines, or AI infrastructure
Familiar with modern MLOps tools and platforms (e.g., Kubeflow, MLflow, Sagemaker, TFX, Airflow)
Experienced in containerization, CI/CD, monitoring, and scaling ML systems
Comfortable identifying weaknesses in operational processes, tooling, or deployment strategies
Available 8 to 20 hours per week
Able to start in the coming weeks

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

MLOpsmachine learning pipelinesautomationmonitoringdeploymentpipeline orchestrationCI/CDmodel servingdrift detectionscaling infrastructure

Soft Skills

evaluating technical validityidentifying gapscreating scenarioscollaborating with stakeholders