We are seeking a meticulous and forward-thinking QA AI Automation Engineer to own end-to-end test automation for our enterprise AI platform. In this role, you will design and execute functional, integration, and LLM-specific regression suites to ensure platform reliability and precision. You will also lead performance/load testing, orchestrate release management, and compile validation evidence for critical delivery milestones, working alongside a platform team that handles security scanning and gate enforcement.

Key Responsibilities:

- Automation Suite Ownership: Build, maintain, and scale comprehensive functional, integration, and regression test suites for backend and distributed systems.

- Performance Load Testing: Design and execute high-scale load and performance testing to ensure system resilience under enterprise workloads.

- CI/CD Integration: Embed automated test execution directly into CI/CD pipelines to support a seamless, continuous integration workflow.

- Accuracy Validation Design: Develop advanced validation strategies (such as precision/recall-style test designs) tailored for accuracy-critical detection systems.

- Release Management: Oversee release management processes across sprint-based delivery cycles, providing necessary validation evidence packages for production milestones.

MINIMUM REQUIREMENTS

- Backend Automation Experience: Proven background in QA and test automation targeting complex backend services or distributed systems.

- Python Test Frameworks: Expert-level mastery of Python test frameworks (specifically pytest or equivalent) with deep experience in API-level automated testing.





- Strategic Integration Testing: Demonstrated ownership of test strategies and regression suites for highly integrated, data-dependent systems.

- Performance Testing at Scale: Hands-on experience with load and performance testing tools such as k6, Locust, JMeter, or equivalent frameworks.

- CI-Driven Development: Experience managing and configuring automated tests to run natively as steps within continuous integration pipelines.

- Sprint-Based Release Management: Solid understanding of release coordination across quick-paced, sprint-based release structures.

- Statistical Test Design: Experience designing evaluation frameworks for data or detection systems using precision/recall and similar statistical validation metrics.

GOOD TO HAVE

- LLM Agent Evaluation: Experience implementing LLM/agent evaluation frameworks and regression suites using golden data sets or reference workloads.

- DLP / PII Testing: Background in validating Data Loss Prevention (DLP) or PII detection engines, specifically optimizing for zero-false-negative benchmarks.

- Cloud Environments: Familiarity running tests inside Kubernetes-based environments or participating directly in Disaster Recovery (DR) simulations.

- NFR Verification: Experience pulling together formal non-functional requirement verification metrics and compiling structured acceptance evidence packages for enterprise clients.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 QA AI Automation Engineer (Hyderabad)
🏢 Nstarx India
📍 Hyderabad