Freelance MLOps Engineer — AI Trainer (Remote)

10x.Team; Greater London; Remote; GBP 82,656 - 137,760; Part time

Job summary

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, and provide concrete feedback to improve accuracy and relevance of ML processes, with flexible scheduling and remote

Qualifications

• Experienced MLOps engineer or manager with deployment and oversight of ML systems.
• Based in the EU or UK.
• Expertise in ML operations strategies, workflow automation, and operational excellence.
• Highly organized with clear feedback delivery.
• Available 8–20 hours per week and reliable in communications.

Responsibilities

• Review and enhance AI-generated content on ML operations, deployment, and workflow optimization.
• Assess the accuracy, technical relevance, and real-world viability of AI-driven ML operations scenarios and recommendations.
• Develop example use cases, deployment strategies, and risk management exercises based on ML industry experience.
• Create multiple scenario variations reflecting perspectives of various ML operations roles, from engineers to stakeholders.
• Identify gaps or flawed assumptions in AI outputs and provide clear, actionable feedback.

Skills

MLOps

Operations leadership

ML deployment

Feedback delivery

Job description

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, and provide concrete feedback to improve accuracy and relevance of ML processes, with flexible scheduling and remote