Pylon Management Consulting
Qa Engineer
Skills
2 months ago
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Important information
Contract type:
Permanent
Location:
Nagpur, Maharashtra, India
Work mode:
Remote
Published on:
4 September 2026
What they need
Job Description: QA Engineer / Test Lead
Experience Required: 4-6 Years
Notice Period: Immediate Joiners Only
Role Overview
We are seeking an experienced QA Engineer / Test Lead responsible for enabling the client's QA engineers and test leads to apply AI across the testing lifecycle.
The role will focus on:
- AI test-case generation
- Regression-test optimization
- Defect triage
- Synthetic test-data generation
- Exploratory-testing ideation
- Bug-report quality improvement
Key Responsibilities
- Enable QA engineers and test leads to apply AI across the testing lifecycle.
- Delivery training on AI test-case generation.
- Support regression-test optimization initiatives.
- Drive defect triage activities.
- Facilitate synthetic test-data generation.
- Guide exploratory-testing ideation using AI.
- Improve bug-report quality through AI-assisted approaches.
- Promote the effective use of GenAI to accelerate testing without compromising coverage or control.
Required Experience & Skills
- 4-6 years of experience in QA Engineering or Test Engineering.
- Strong QA background with experience in manual and automated testing.
- Understanding of test-management processes and defect lifecycle management.
- Experience with enterprise quality practices.
- Exposure to AI and GenAI tools used within testing environments.
- Strong understanding of the differences between traditional QA practices and AI-enabled QA approaches.
- Knowledge of AI test-case generation, test optimisation, and AI-assisted testing techniques.
- Practical experience using GenAI to accelerate testing while maintaining quality, coverage, and control.
- Good communication and stakeholder management skills.
- Ability to clearly explain AI concepts and tools to engineering and business stakeholders.
Note: Candidates must have AI exposure, strong communication skills, an understanding of AI tools, and appreciation of the differences between non-AI QA and AI-enabled QA practices.
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