The work
You will help define how AI tools should handle IT ticketing, incident management, and software support workflows. The work combines IT operations knowledge with structured spreadsheet modeling and careful review of AI-generated technical outputs.
• Set operational discovery rules and technical guardrails for IT support AI tools.
• Build and verify IT dashboards, SLA tracking sheets, asset lifecycle rollups, and triage escalation logic in Google Sheets or Excel.
• Create realistic IT service scenarios and check reference materials such as incident logs, post-mortems, and support playbooks.
• Review, score, and improve AI-generated technical outputs against software engineering and ITIL practices.
What it pays and takes
The listing does not specify a pay rate. This is a remote contractor role lasting up to two weeks, with the schedule and experience requirements below.
• Pay: Not stated in the listing.
• Schedule: The role lists 40 hours per week, with 8 hours of overlap with Pacific Time.
• Time commitment: 20+ hours per week is listed in the role details, and part-time work is acceptable.
• Availability: Weekend on-call availability is required.
• Location: Remote worldwide.
• Equipment: You need a personal desktop or laptop and stable, high-speed internet.
• Language: English.
• Experience: At least 3 years in software engineering, IT operations, or technical support management.
• Knowledge: Issue trackers, ITSM helpdesks, SLA policies, escalation paths, and asset lifecycle management.
• Communication: Strong, concise professional writing and the ability to turn triage workflows, SLA timers, and incident rules into spreadsheet formulas and fixed parameters.
• Education: A bachelor's degree or equivalent practical experience.
• Helpful background: AI evaluation, data annotation, content review, quality assurance, or a related analytical role.
How it works
Apply on OpenTrain with your resume and then complete the application on the hiring site.
About AI training work
AI training work is the human process of preparing examples, checking outputs, and setting rules that help AI systems work better. Experienced professionals are needed to judge whether technical answers follow real workplace standards and workflows.