Step into a role where you help teams build Generative AI solutions that are not only powerful, but also trustworthy, safe, and testable. As part of a collaborative consulting environment, you’ll work closely with product, engineering, and risk stakeholders to design practical assurance approaches for LLM-powered systems—balancing innovation with reliability. You’ll contribute to real-world AI validation efforts, from defining test strategies and evaluation metrics to executing red teaming exercises and documenting outcomes that leadership can act on. If you enjoy combining structured testing discipline with the fast-evolving world of GenAI, this is a chance to shape how AI quality, safety, and responsibility are measured and improved—while learning alongside motivated teams solving meaningful problems. • 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.