MLOps Engineer - AI Trainer - Freelance - 8-20hrs/week - Remote, Amsterdam

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.

In simple terms: you will assess and improve AI‑generated content to ensure it matches real‑world MLOps standards and workflows. Your work will directly enhance the quality and reliability of AI systems for MLOps tasks.

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.

This is a fully remote, flexible role—ideal alongside other commitments.

Why join?

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.

Screening process 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.

If approved, you’ll be onboarded and can start shortly after.

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