IT Operations Workflow AI Training Expert

You will turn real-world IT operations logic into structured reference materials that AI systems can use and be tested against. You will also review AI-generated applications and identify errors or edge cases.

The work covers IT service management, incident handling, SLA tracking, asset records, and support workflows.

• Build reference models for IT dashboards, SLA tracking sheets, asset rollups, and automated incident triage.
• Create datasets from incident logs, post-mortems, support playbooks, and parameter sheets.
• Write clear rules and technical limits for ticketing and ITIL workflows.
• Test AI-generated applications, score their outputs, and identify edge cases.

What it pays and takes

The listing does not provide a pay rate. The role description calls for 40 hours per week for up to two weeks, while the structured listing specifies 20 or more hours per week and allows part-time work. Weekend availability and eight hours of overlap with Pacific Time are required.

• Contractor role with part-time work listed as an option.
• Open worldwide; English is required.
• Availability for weekends and eight hours of overlap with Pacific Time.
• At least three years of experience in IT operations, software management, technical support leadership, or ITSM or helpdesk administration.
• Strong knowledge of incident triage, SLA policies, escalation paths, issue trackers, and asset lifecycle tracking.
• Ability to turn incident workflows and SLA calculations into spreadsheet formulas and parameters.
• Reliable laptop or desktop computer with high-speed internet.
• Bachelor's degree or equivalent practical experience.
• AI evaluation, data annotation, content review, or quality assurance experience is helpful but not 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, instructions, and evaluations. People with practical IT expertise help define accurate workflows and judge whether AI outputs follow real operational rules.