Role - QA Lead

Experience - 8 - 10 Yrs

Location - Pune

Notice Period - Immediate Joiners

Project Details

Project: Enterprise Platform Quality Engineering & Reliability Assurance The project focuses on ensuring quality, scalability, performance, and reliability of a distributed microservices-based platform through advanced test automation, performance engineering, observability, and AI-assisted quality practices. The QA team works closely with Engineering, DevOps, and SRE teams to validate complex customer workflows, event-driven architectures, data pipelines, and production environments. The engagement emphasizes high-quality releases, proactive defect prevention, root cause analysis, and continuous improvements in platform reliability through automation, monitoring, and performance validation.

Roles & Responsibilities:

1. Test Automation Strategy & Framework Development

Designed and maintained scalable automation frameworks for API, integration, and end-to-end testing using Selenium, Playwright, Postman, RestAssured, PyTest, and TestNG, improving regression coverage across business-critical workflows.

2. Microservices & System-Level Validation

Developed comprehensive regression suites to validate event-driven architectures, asynchronous processing, data pipelines,



and cross-service customer journeys, ensuring seamless functionality across distributed systems.

3. CI/CD Quality Enablement

Integrated automated test suites Jenkins, GitHub Actions, and GitLab CI pipelines, enabling continuous testing, faster feedback cycles, and high-confidence production releases.

4. Performance Engineering & Scalability Testing

Designed and executed load, stress, endurance, and performance testing using JMeter and K6 to identify bottlenecks, validate system scalability, and ensure platform stability under peak workloads.

5. Observability, Monitoring & Production Diagnostics

Leveraged Datadog and Splunk to monitor application health, investigate production incidents, perform root cause analysis, reproduce defects, and identify quality gaps based on incident trends and operational insights.

6. AI-Driven Quality Engineering & Continuous Improvement

Utilized AI-assisted tools for test case generation, automation development, defect clustering, log analysis, anomaly detection, and test coverage optimization, driving higher testing efficiency, improved defect detection, and enhanced release quality.

📌 Qa Lead (Pune)
🏢 Allegis Group
📍 Pune