Freelance MLOps Engineer β€” AI Trainer (Remote) in London

Freelance MLOps Engineer β€” AI Trainer (Remote) in London

London Freelance 30000 - 40000 Β£ / year (est.) Remote
1

At a Glance

  • Tasks: Train AI systems and validate ML workflows while working remotely.
  • Company: Join a forward-thinking team focused on real-world AI applications.
  • Benefits: Flexible hours, remote work, and the chance to shape AI technology.
  • Other info: Part-time role with opportunities for growth in the AI field.
  • Why this job: Make a tangible impact on AI deployments and enhance machine learning accuracy.
  • Qualifications: Experience in MLOps and a passion for AI training.

The predicted salary is between 30000 - 40000 Β£ per year.

10x Team is seeking a Freelance Machine Learning Operations Engineer to train AI systems and validate ML workflows. The role offers 8–20 hours per week, fully remote, based in the EU/UK, and focuses on shaping real-world AI deployments and decision-making through practical ML operations expertise.

As an AI trainer, you will:

  • Review AI-generated outputs
  • Develop use-case scenarios
  • Provide concrete feedback to improve accuracy and relevance of ML processes

The position offers flexible scheduling and remote work.

Freelance MLOps Engineer β€” AI Trainer (Remote) in London employer: 10x.Team

10x.team is an exceptional employer for experienced sales executives looking to make a meaningful impact in the AI industry. With a flexible, fully remote freelance role that allows you to balance your commitments, you'll have the opportunity to apply your expertise in a cutting-edge environment while benefiting from free access to our in-house AI Academy for professional growth. Our supportive work culture fosters collaboration and innovation, ensuring that you can contribute significantly to advancing AI-driven business applications.

1

Contact Details:

10x.Team Recruitment Team

We think you need these skills to ace Freelance MLOps Engineer β€” AI Trainer (Remote) in London

Machine Learning Operations (MLOps)
AI Training
Validation of ML Workflows
Reviewing AI-generated Outputs
Developing Use-case Scenarios
Providing Feedback for ML Processes
Accuracy Improvement