Senior QA Engineer

About this role

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.