AI Test Technician

Data & Model Evaluation: Prepare, clean, and run test datasets through AI models (e.g., LLMs, Computer Vision, Speech, or Predictive Models) to measure performance against baseline standards.

Edge Case & Stress Testing: Design scenarios to deliberately challenge the system (adversarial testing, prompt injection, edge-case validation, halluncination detection).

Bias & Safety Checking: Evaluate outputs for potential bias, unfairness, toxicity, or safety risks to ensure compliance with ethical guidelines.

Bug Tracking & Reporting: Document failures, latency spikes, output drifts, and inconsistent behavior, collaborating closely with Data Scientists and Machine Learning Engineers.

Test Automation: Write scripts to automate continuous evaluation pipelines and regression test suites.

Education: Associate’s or Bachelor’s degree in Computer Science, Data Analytics, Information Technology, or a related field.

Experience: 0–3 years in software testing, QA, data validation, or junior technical support roles.