Role Summary

We are seeking a QA Lead with 10 years of solid 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 modern QA engineering practices

📌 Qa Lead (Pune)
🏢 Wintech Services ( India) Private
📍 Pune