Senior QA engineer (AI Platform)

About Gruve Gruve is an innovative software services startup dedicated to transforming enterprises to AI powerhouses. We specialize in cybersecurity, customer experience, cloud infrastructure, and advanced technologies such as Large Language Models (LLMs). Our mission is to assist our customers in their business strategies utilizing their data to make more intelligent decisions. As a well-funded early-stage startup, Gruve offers a dynamic environment with strong customer and partner networks. Position Summary: The Senior QA Engineer will own end-to-end quality assurance for PulseAI, Gruve's on-premises AI infrastructure platform. The role requires strong product knowledge and the ability to design and execute test strategies across a complex, multi-tenant platform covering role-based access control, GPU resource management, Kubernetes-native endpoint lifecycle, audit logging, usage reporting, and workflow automation. The ideal candidate will collaborate closely with engineering and product teams to define acceptance criteria, build automated regression suites, track test metrics, lead UAT cycles, and ensure every delivery milestone meets the agreed quality bar before sign-off. Key Roles & Responsibilities: Own the end-to-end QA strategy for the platform, aligning test scope with product requirements, feature acceptance criteria, and milestone gate requirements Translate product requirements and user stories into structured test plans, test cases, and traceability matrices covering all platform roles and access levels Define, track, and report test metrics (coverage, pass/fail rates, defect density, and escape rate) to inform release readiness Validate the multi-role RBAC model: role assignment, hierarchical access enforcement, cross-project role combinations, and API-level permission controls Test GPU resource quota management across multiple organisational levels, including over-quota rejection scenarios and quota enforcement when resources are actively in use Execute end-to-end testing of the AI inference endpoint deployment pipeline: configuration validation, resource availability checks, GPU allocation, Kubernetes pod provisioning, and URL assignment Verify multi-tenant isolation: namespace separation, cross-tenant visibility restrictions, and project-scoped resource access controls Test user account management workflows: create, update, delete, bulk import, role assignment, SSO integration, and two-factor authentication enforcement Validate GPU hardware compatibility across supported NVIDIA and AMD GPU models for MIG partitioning and fractional allocation modes Test secrets and credential management: encryption at rest, masking in logs and API responses, and rotation without service disruption Validate the audit logging system: completeness across all action types, tamper-evidence, configurable retention, and external log forwarding Test REST management APIs, usage and chargeback reporting APIs, and OpenAI-compatible AI inference endpoints for functional correctness and contract compliance Build and maintain automated regression suites (API, UI, and integration) using Selenium and Playwright, integrated into the CI/CD pipeline Coordinate and execute UAT cycles, validate acceptance criteria before sign-off, and liaise with internal and partner engineering teams on shared testing responsibilities Execute performance benchmark tests covering inference latency, throughput, and concurrent request handling under load Basic Qualifications: Education: B.E / B.Tech or equivalent in Computer Science, Information Technology, or a related discipline 5-8 years of experience in QA / Software Testing, with strong product knowledge of AI/ML infrastructure platforms Proven experience creating QA test plans, writing test cases, and tracking test metrics/reporting Hands-on experience working on Linux systems in a Kubernetes environment Strong automation skills — able to automate all test cases (functional, regression, API, and UI) Hands-on…