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 workplace 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 automation experience with Selenium and Playwright

Working knowledge of role-based access control patterns, SSO/token-based authentication, and multi-factor authentication testing Preferred Qualifications/skills Hands-on experience testing AI/ML models or model deployment pipelines

Experience testing on NVIDIA GPUs

Familiarity with GitOps deployment patterns and deployment audit trail testing

Experience with workflow automation tools

ISTQB Advanced Level or equivalent certification

Domain knowledge in Networking, Infrastructure, or Security is a plus Why Gruve At Gruve, we foster a culture of innovation, collaboration, and continuous learning. We are committed to building a diverse and inclusive workplace where everyone can thrive and contribute their best work. If you’re passionate about technology and eager to make an impact, we’d love to hear from you.

Gruve is an equal opportunity employer. We welcome applicants from all backgrounds and thank all who apply; however, only those selected for an interview will be contacted.

📌 Senior QA engineer (AI Platform) (Pune)
🏢 Gruve
📍 Pune