AI Summary
Seeking experienced freelance MLOps engineers in the EU/UK for 8-20 hrs/week. You will review and refine AI-generated content for MLOps workflows, ensuring technical validity and industry best practices. This remote role offers flexible hours to enhance AI systems for MLOps tasks.
Key Highlights
Freelance MLOps Engineer role with flexible hours (8-20 hrs/week).
Focus on reviewing and improving AI-generated MLOps content.
Remote position for candidates based in the EU or UK.
Key Responsibilities
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.
Technical Skills Required
Benefits & Perks
Flexible hours
Fully remote
Job Description
Freelance | 8–20 hrs/week | Remote (EU/UK)
Are you an experienced MLOps engineer interested in applying your expertise to cutting-edge AI systems? Do you have 8 to 20 hours a week available alongside your current projects or consulting work?
We are seeking freelance MLOps engineers based in the EU or UK to help improve advanced AI models.
What You’ll Be Doing
We are 10x.team, a platform for fractional and freelance professionals. We partner with leading AI labs to advance the capabilities of large AI systems.
Your Role Is Both Practical And High-impact. You Will
- 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.
Interested in remote work opportunities in Machine Learning & AI? Discover Machine Learning & AI Remote Jobs featuring exclusive positions from top companies that offer flexible work arrangements.
You Are
Who this is for
- 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
Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.
- 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
Our process is straightforward and fully guided. After applying, you will complete:
- A short AI-based interview
- A brief written evaluation focused on MLOps reasoning and methodology
- A compliance check to verify your identity and professional background
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