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Tier-1 brand and Bangalore location increase competition, but niche AI-testing focus reduces applicant pool.
AI-testing skills are moderately transferable, but GenAI-specific tools create some industry specialization.
Specialized AI testing skills and financial services context imply strict technical and domain filters.
Lead testing and quality assurance of AI-powered applications, including Generative AI, Retrieval Augmented Generation (RAG) systems, AI agents, and Large Language Model (LLM) applications.
Design and execute comprehensive evaluation frameworks and testing strategies covering AI accuracy, robustness, hallucination detection, safety controls, and adversarial testing.
Develop and maintain automated AI evaluation pipelines, benchmark datasets, and collaborate with cross-functional teams to validate AI-enabled features and solutions.
Experience required: Hands-on experience testing AI/GenAI applications in production or pre-production.
Strong knowledge of Large Language Models, Generative AI concepts, prompt engineering, embeddings, vector databases, and RAG architectures.
Proficiency with AI evaluation frameworks such as DeepEval, RAGAS, LangSmith, Phoenix, or similar tools.
Experience in developing automation frameworks and test suites using Python and open-source testing tools.
Has deep expertise in AI/GenAI testing with focus on LLMs, RAG systems, AI agents, and AI safety measures.
Operates effectively at the intersection of AI technology and quality engineering with strong collaboration skills involving Product Owners, AI Engineers, and Developers.
Experienced in building automated AI evaluation pipelines integrated with CI/CD and managing AI observability and security risks such as prompt injection and data leakage.