RPA-GenAI Trust and Assurance Consultant

Job description

Educational Requirements Bachelor of Engineering Service Line Infosys Quality Engineering Responsibilities - 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. Additional Responsibilities: - 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. Technical and Professional Requirements: - BTech/BE or equivalent technical degree. - 39 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. Preferred Skills: - Technology->Testing Technologyes->Test Automation Technology->Testing - Technology->AI-Data science->Machine Learning - Technology->AI-Generative AI->Generative AI - Basic - Technology->AI-Responsible AI->Responsible AI - Technology->AI-Generative AI->Prompt Engineering - Technology->AI-Agentic AI->Agent Engineering - Technology->AI-AI Engineering->LLMOps