QA Lead - Manual, Automation & AI Testing

About the role: We are looking for an experienced and hands-on QA Lead with 7+ years of experience in software quality assurance. The ideal candidate must have strong expertise in manual testing, automation testing, Python, test automation frameworks, and AI-powered product testing. The candidate should have experience working in a product-based technology company and be capable of owning the complete quality lifecycle—from requirement analysis and test planning to automation, release sign-off, AI evaluation, and production-quality monitoring. Role: QA Lead Requirement: 1 Location: Bangalore (Domlur | WFO 5 days) Experience: 7+ years Requirements Responsibilities: - Own the overall quality strategy for the assigned products and engineering teams - Lead manual and automation testing across web applications, mobile applications, APIs, backend services, AI features, and third-party integrations - Design, develop, and maintain scalable automation frameworks using Python - Create comprehensive test plans, test scenarios, test cases, and release-quality reports - Perform functional, regression, integration, API, database, exploratory, and performance testing - Define testing strategies for AI/ML and Generative AI features, including chatbots, recommendation systems, search, summarisation, classification, and content-generation workflows - Validate AI-generated responses for accuracy, relevance, consistency, completeness, safety, and business-rule compliance - Test AI systems for hallucinations, inappropriate responses, prompt injection, data leakage, bias, and edge cases - Build automated evaluation frameworks and datasets for testing LLM and AI-powered features - Test Retrieval-Augmented Generation (RAG) workflows, including document retrieval, context relevance, response grounding, and citation accuracy - Validate AI model and third-party LLM API integrations for reliability, latency, error handling, rate limits, token usage, and cost - Establish baseline quality metrics and regression suites for AI-generated outputs - Review product requirements, prompts, workflows, and technical designs to identify gaps and risks early in the development lifecycle - Define and track quality metrics such as defect leakage, automation coverage, regression effectiveness, release readiness, AI response accuracy, hallucination rate, and latency - Work closely with Product Managers, Developers, DevOps, Data Scientists, and AI/ML Engineers - Lead release validation, QA sign-off, production sanity testing, and post-release monitoring - Analyse production defects, support root-cause analysis, and implement preventive measures - Mentor QA engineers and promote a strong quality-first culture across Product and Engineering teams Must-Have Qualifications - 6+ years of experience in software testing and quality assurance - Strong hands-on expertise in both manual and automation testing - Proficiency in Python for developing automation frameworks and test utilities - Strong experience with tools and frameworks such as Pytest, Selenium, Playwright, Appium, or Robot Framework - Experience in API testing using Postman, Python Requests, REST Assured, or similar tools - Good knowledge of database testing and strong proficiency in SQL - Strong understanding of testing methodologies, QA processes, SDLC, and STLC - Experience with functional, integration, regression, system, exploratory, and end-to-end testing - Experience integrating automated tests with CI/CD pipelines - Hands-on experience with Git, Jenkins, GitHub Actions, Jira, or similar tools - Experience working in a product-based company and testing customer-facing products at scale - Understanding of AI/ML concepts and experience testing AI-powered or Generative AI features - Understanding of LLM behaviour, including non-deterministic outputs, hallucinations, context limitations, and prompt sensitivity - Ability to design test datasets, evaluation criteria, and quality metrics for AI-generated outputs - Strong analytical, debugging, problem-solving, and risk-identification skills - Good communication, stakeholder-management, and team-leadership capabilities - Ability to take complete ownership of product quality and release sign-off Good to Have - Experience testing LLM-based applications, AI chatbots, RAG systems, recommendation engines, or semantic search - Experience with AI evaluation and observability tools such as LangSmith, DeepEval, Ragas, Promptfoo, TruLens, or similar platforms - Familiarity with models and APIs from OpenAI, Gemini, Claude, or open-source LLM platforms - Knowledge of prompt engineering and automated prompt-regression testing - Experience evaluating AI systems for responsible AI, privacy, security, fairness, and bias - Experience with performance-testing tools such as JMeter, Locust, or k6 - Experience testing microservices, distributed systems, and event-driven architectures - Exposure to cloud platforms such as GCP, AWS, or Azure - Knowledge of Docker, Kubernetes, Kafka, or similar technologies - Experience with monitoring tools such as Grafana, Kibana, or Datadog - Experience in recruitment technology, marketplaces, SaaS, or other high-scale products Education Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field. Ideal Candidate The ideal candidate is a hands-on QA leader who combines strong technical expertise with product and AI-quality thinking. They should be comfortable writing automation code, performing detailed manual testing, evaluating AI-generated responses, challenging requirements, identifying customer-impacting risks, and guiding teams towards reliable, safe, scalable, and high-quality product delivery.