Role Summary: We are seeking a QA Lead with 10+ years of strong expertise in Automation,
Performance Engineering, and Observability to ensure platform reliability across a
distributed microservices architecture.
The role will drive scalable test automation, system performance validation, and
production diagnostics, while leveraging AI-assisted testing and analytics to improve
defect detection and testing efficiency.
Extensive experience in AI and AI implementation
Hands-on experience with AI Agents and AI-driven workflows

The QA Lead will work closely with Engineering, DevOps, and SRE teams to ensure
high-quality releases and improved system reliability.
Key Responsibilities:
Test Automation Leadership.
Design and maintain automation frameworks for API, integration, and end-to-end
testing.
Develop system-level regression suites covering event-driven workflows, data pipelines,
and asynchronous processing
Build regression coverage for cross-service customer workflows.
Integrate automation into CI/CD pipelines.
Drive automation best practices across teams.
Tools:
API Testing: Postman, RestAssured
UI Automation: Selenium, Playwright
Frameworks: PyTest, TestNG
CI/CD: Jenkins, GitHub Actions, GitLab CI
Performance Engineering:
Design and execute load, stress, and endurance testing.




Identify performance bottlenecks in distributed services
Validate system scalability and stability before releases.
Tools:
JMeter
K6
Observability & Production Diagnostics:

Monitor system behaviour using observability platforms.
Investigate production incidents and perform root cause analysis.
Reproduce production defects in lower environments.
Identify testing gaps based on incident patterns.
Tools:
Datadog
Splunk
AI-Enabled Quality Engineering (Nice to Have):
Experience leveraging AI tools to improve testing efficiency and defect detection.
Examples include:
AI-assisted test case generation.
AI-assisted automation development (GitHub Copilot or similar tools)
AI-driven defect clustering and log analysis.
AI-powered anomaly detection from observability data.
Using AI to identify gaps in test coverage and customer workflows.
Technical Skills:
Programming: Python, Java, JavaScript
Test automation frameworks for API and E2E testing
Performance testing tools
CI/CD pipelines and DevOps workflows
Observability and monitoring tools
Experience with microservices architectures
Preferred Experience:
Containerized environments (Docker, Kubernetes)
Working with SRE / platform teams
Exposure to AI-assisted testing tools and up-to-date QA engineering practices'

📌 QA Lead Engineer (Pune)
🏢 Wintech Services ( India) Private
📍 Pune