Key skills
AirflowCloudAIMachine LearningMLMLOpsMLflowKubeflowSageMakerCI/CDCollaboration
About this role
Role Overview
- 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
Tech Stack
- Airflow
- Cloud
Benefits
- Flexible hours
- Fully remote
- Apply your MLOps expertise to real-world AI systems
- Contribute to AI products used at scale
- Structured onboarding and clear project scope
- Potential for long-term collaboration based on performance