AI Red Teamer and LLM Safety Specialist
We are seeking analytical professionals with hands-on experience in Red Teaming, Prompt Evaluation, and AI/LLM Quality Assurance. The ideal candidate will rigorously test and evaluate AI-generated content to identify vulnerabilities and ensure compliance with safety, ethical, and quality standards.
Job Description:
The selected individual will be responsible for conducting Red Teaming exercises to identify adversarial outputs from large language models (LLMs). They will also evaluate and stress-test AI prompts across multiple domains to uncover potential failure modes. Furthermore, they will develop test cases to assess accuracy, bias, toxicity, hallucinations, and misuse potential in AI-generated responses. Additionally, they will collaborate with data scientists to report risks and suggest mitigations.
Key Responsibilities:
* Conduct Red Teaming exercises to identify adversarial outputs from large language models (LLMs).
* Evaluate and stress-test AI prompts across multiple domains to uncover potential failure modes.
* Develop test cases to assess accuracy, bias, toxicity, hallucinations, and misuse potential in AI-generated responses.
* Collaborate with data scientists to report risks and suggest mitigations.
* Perform manual QA and content validation ensuring factual consistency and guideline adherence.
* Create evaluation frameworks for prompt performance and safety compliance.
* Document findings with high clarity and structure.
Requirements:
The ideal candidate should have a proven experience in AI red teaming, LLM safety testing, or adversarial prompt design. Familiarity with prompt engineering, NLP tasks, and ethical considerations in generative AI is also essential. A strong background in Quality Assurance, content review, or test case development for AI/ML systems is required. Understanding of LLM behaviors and model evaluation metrics is necessary. Excellent critical thinking, pattern recognition, and analytical writing skills are also expected.