- BTech/BE or equivalent technical degree.
- 3–9 years of experience in testing/quality engineering, with hands-on exposure to Generative AI or LLM testing initiatives.
- Working knowledge of AI testing concepts, including test design, evaluation metrics, and result interpretation for LLM outputs.
- Experience contributing to structured documentation such as test plans, test evidence, and defect/risk reporting.
- Define and execute testing strategies for Generative AI/LLM systems, covering functional, non-functional, and safety-focused validation.
- Design evaluation frameworks and test suites using tools such as DeepEval to measure quality, robustness, and consistency of model outputs.
- Plan and conduct red teaming exercises to identify vulnerabilities (e.g., prompt injection, jailbreaks, harmful content, data leakage) and recommend mitigations.
- Establish Responsible AI assurance checks aligned to fairness, transparency, privacy, and safety expectations.
- Create clear test plans, evidence, dashboards, and reports that communicate risks, findings, and remediation priorities to stakeholders.
- Collaborate with engineering and product teams to integrate AI testing into delivery pipelines and improve release readiness criteria.
- Support incident triage and root-cause analysis for model behavior issues, regressions, and evaluation drift across versions.
- Proven experience executing red teaming for LLM applications and translating findings into actionable controls and mitigations.
- Practical experience implementing Responsible AI practices and assurance workflows across the AI lifecycle.
- Strong understanding of LLM failure modes (hallucinations, toxicity, bias, prompt sensitivity) and methods to test and reduce them.
- Experience building automated evaluation pipelines and regression suites for LLM systems using DeepEval or similar frameworks.
- Consulting experience: stakeholder management, requirement discovery, and delivering clear outcomes under ambiguity.