QA Engineer

Job Summary

We are looking for a QA Engineer with 2+ years of experience with strong knowledge of SDLC/STLC, test execution, defect management, test automation, and QA delivery . The candidate should demonstrate strong ownership, effective cross-functional collaboration, and the ability to leverage AI tools to improve QA efficiency and productivity .

Key Responsibilities

- Apply strong knowledge of SDLC/STLC, test planning, test case design, execution, and defect lifecycle .

- Perform Unit, Integration, Functional, End-to-End (E2E), Regression, Smoke, Sanity, and Non-Functional testing .

- Take end-to-end ownership of QA delivery , ensuring testing activities are completed within timelines and quality expectations.

- Collaborate closely with developers to identify, reproduce, track, retest, and close defects .

- Communicate testing status, risks, blockers, and delivery impact to relevant stakeholders.

- Develop and maintain automated test scripts using Selenium and/or Playwright .

- Leverage AI tools for test case generation, automation, test data creation, defect analysis, documentation, and other QA productivity improvements.

- Participate in Agile ceremonies and collaborate effectively with Product, Development, and Business teams.

- Support production issue analysis, RCA, and preventive QA measures .

Required Skills

- 2+ years of experience in Software QA/Testing .

- Strong understanding of SDLC, STLC, and defect management .

- Strong ownership and accountability for QA delivery .

- Good communication, analytical, and cross-functional collaboration skills.

- Hands-on knowledge of Selenium and/or Playwright .

- Good understanding of functional and non-functional testing methodologies .

- Basic knowledge of API testing and SQL .

- Experience with Jira, Azure DevOps, or similar defect-tracking tools .

- Experience using AI-powered tools to improve QA efficiency and automation .

- Experience working on AI/Agentic AI, RPA, or Document Automation projects .

Must Should Have this

- Practical knowledge of AI-assisted QA tools and prompt testing .

- Basic understanding of AI Knowledge Bases (KB), RAG, retrieval, grounding, and hallucination testing .

- Ability to design AI-specific test scenarios and evaluate AI-generated responses .

- Experience working on AI/Agentic AI, RPA, or Document Automation projects .

- Experience testing AI Agents, LLM-based applications, RAG pipelines, or AI-powered business workflows .