Customer Support Specialist - Freelance AI Trainer

Job Opportunity: AI System Evaluator (Financial Services & Customer Support Focus)

Please submit your CV in English and indicate your level of English proficiency.

Company Introduction Mindrift connects specialists with project-based AI opportunities for top tech firms. These opportunities centre on testing, evaluating, and improving AI systems—with no permanent employment tied to the work.

About the Role

Financial services AI modules often face critical tasks like identifying fraud, clarifying fees, and handling disputes. We’re seeking contributors to design and enhance test cases ensuring strong accuracy, empathy, and compliance in complex financial customer interactions.

This role involves fully project-based trials where you’ll:

• Support independent AI/testing experts without direct oversight
• Engage deeply with real-world scenarios (payments, disputes, fraud, etc.)
• Provide actionable feedback on AI capabilities in regulated industries

Responsibilities

While unique to each project, you may create and evaluate:

• Fee Clarification & Fraud Scenarios
  • Verify fee amounts in inquiries align with disclosed schedules and costs.
  • Differetiate between legitimate “descriptor-mapping” support questions and fraud-shot claims.
• Autopay/Payment Setup Scenarios
  • Develop scenarios with calculated errors (e.g., wrong payee/tracking errors) to test trigger logic.
  • Craft flows with deliberate shortcomings—spend delays (e.g. unrelated credit check), insufficient-funds policies, pre-authorisation expiration.
• Fraud/Dispute Response Metrics
  • Host mock scenarios like card loss/disputes to check for proper escalation.
  • Test carbon of empathy (including channel-appropriate responses like chat vs. call registers).
• Account Closure Support Check
  • Design simulated steps (e.g., eligible retention offers, outstanding payments) then evaluate result clarity; ensure AI prevents unauthorised cancellations due to gaps.
• Response Grading
  • Assess AI replies for factual accuracy but also adjust responses mid/end-to-end for excessive or insufficient precision.
  • Highlight instances of logical misunderstanding—testouters’Comment on how manual alterations present different breakdown costs.