About the role:
We are looking for an experienced and hands-on Senior QA Engineer 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: Senior QA Engineer
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.