Casino Gaming Operations AI Evaluator (Table Games Managers)

You will help assess whether AI-generated content reflects accurate casino gaming operations. The work uses your experience with table games, staff supervision, procedures, guest service, and regulated gaming environments.

The duties and qualifications for this role should be reviewed before applying because the role title and stated experience focus may differ.

• Write job-related questions based on real-world table games operations and gaming leadership.
• Review AI-generated answers for accuracy, relevance, sound procedures, and casino service standards.
• Identify guidance that would be incorrect or unrealistic for dealers, pit staff, floor staff, or gaming operations teams.
• Write clear feedback, follow project guidelines, and protect sensitive or proprietary information.

What it pays and takes

This is a remote, part-time independent contractor role with flexible, asynchronous scheduling. Projects may open periodically throughout the year, and there is no minimum hour requirement.

• Pay: Up to $90 per hour.
• Project schedule: Approximately 5 to 20 hours per week when an active project is assigned.
• Location: Open to candidates in the United States.
• Language: Fluent in English.
• Experience: At least four years of professional experience in casino games management or closely related gaming operations leadership.
• Management experience: Oversight of table games operations and supervision of dealers, pit staff, floor staff, or comparable teams.
• Knowledge: Gaming procedures, regulatory compliance, and guest-service expectations in casino, cruise-ship, or other regulated gaming environments.
• Other requirements: Strong written communication, attention to detail, independent judgment, reliability, and discretion.
• AI experience: No prior AI or technical experience is required.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training is the human work behind systems that learn from examples, including reviewing written answers and rating whether they are accurate and useful. People with practical experience are paid to help identify mistakes and make AI behavior more reliable.