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

10x.Team; Amsterdam; Remote; EUR 83,000 - 124,000; Part time

Benefits offered by this job

Flexible hours

Fully remote

Structured onboarding

Job summary

A leading freelance platform is seeking an experienced MLOps engineer for a remote role with flexible hours (8-20 hours/week). Responsibilities include reviewing AI-generated content, evaluating workflows, and drafting scenarios for MLOps tasks. Ideal candidates should have several years of experience in ML operations and be familiar with MLOps tools like Kubeflow and Sagemaker. Great opportunity for those looking to apply their expertise in cutting-edge AI systems remotely while managing other commitments.

Qualifications

• Several years of experience in machine learning operations.
• Familiar with modern MLOps tools and platforms.
• Comfortable identifying weaknesses in operational processes.
• Available 8 to 20 hours per week.
• Experience in containerization, CI/CD, monitoring, and scaling ML systems.

Responsibilities

• Review and refine AI-generated content related to MLOps workflows.
• Evaluate outputs for technical validity and best practices.
• Draft realistic scenarios covering pipeline orchestration.
• 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.

Skills

MLOps expertise

Machine Learning pipelines

CI/CD experience

Containerization

Monitoring ML systems

Monitoring

Scaling ML systems

AI infrastructure

Tools

Kubeflow

MLflow

Sagemaker

TFX

Airflow

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.

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.