Senior Backend Engineer (Python, SQL & AI Integration)

QA Engineer

Role Overview Seeking a QA Engineer to define and execute the end-to-end quality assurance strategy for the UC-003 Billing Prep program—covering rules engine logic, AI model outputs, API integrations, and the human review workbench. The engineer will drive test automation, coordinate business UAT, and serve as the final quality gate before deployment.

Role Details

• Job Title: QA Engineer
• Experience: 3-5
• Reporting To: QA Lead
• Engagement Type: Full-time
• Location: Hyderabad Office

Required Qualifications (Must Haves)

• 3+ years of agile QA engineering experience with test automation expertise in Python (pytest, requests) for API/backend services.
• Hands-on experience with REST API tools (Postman/Insomnia), contract validation, and strong SQL skills for database state and transformation testing.
• Familiarity with GCP services (Cloud SQL, Pub/Sub, Cloud Monitoring) and CI/CD integration for automated test execution (Cloud Build, GitHub Actions).
• Experience testing financial/billing pipelines (invoices, charge reconciliation) and defect tracking platforms (Jira, TestRail).
• Proven track record designing and running business-led UAT and testing AI/ML-augmented workflows (model outputs, confidence thresholds, escalation paths).

Good to Have

• ISTQB Foundation or Advanced certification.
• Direct experience testing GCP-native architectures (Cloud Run, Cloud SQL, Pub/Sub, Cloud Scheduler).
• Domain exposure to real estate, facilities management, or professional services billing environments.
• Familiarity with performance testing tools (Locust, k6, JMeter), load profiling, and financial data quality frameworks.

Key Responsibilities

• Test Strategy & UAT Execution: Develop end-to-end test strategies for all pipeline components, establish UAT entry/exit criteria, and facilitate business UAT sessions to drive completion by late November 2026.
• Automation & Validation: Build automated API/rules engine regression suites on Cloud Run, validate AI model accuracy thresholds, and run performance tests against realistic BIL file volumes.
• Defect & Defect Management: Triage, log, and route UAT bugs with clear reproduction steps, track resolution progress, and validate fixes prior to production release.
• Governance & Pipeline Quality: Enforce deployment-blocking CI/CD quality gates, produce sprint test metrics, and verify security/access controls alongside security teams.