About the Role

Neysa is building Velocis — an AI Infrastructure and PaaS platform that powers inference, orchestration, and multi-tenant GPU workloads at scale. This is not a conventional software QA role.

The QA Lead will own the end-to-end quality strategy for Velocis — a distributed, Kubernetes-native platform spanning IaaS provisioning, AI-PaaS services, multi-tenant control planes, and real-time inference APIs. This is a hands-on leadership role: you will set the quality bar, build the team, define the toolchain, and be accountable for what ships.

Key Responsibilities

Quality Strategy & Engineering Leadership

- Define and execute Neysa's Quality Engineering strategy across the Velocis platform

- Establish QA processes, standards, and release governance across engineering teams

- Build and mentor a high-performing QA organization; establish the hiring bar and career ladder

- Drive a quality-first culture across Engineering, Product, and Platform Operations

- Partner with Engineering Managers, Product, and SRE on release readiness and go/no-go decisions

Test Planning & Execution

- Own end-to-end test strategy for all Velocis platform releases

- Review PRDs, technical designs, and architecture documents for testability — early, not late

- Define test coverage across: Functional, Integration, Regression, Performance, Load, Security, API, and End-to-End testing

- Apply risk-based testing to prioritize coverage on high-blast-radius surfaces

Automation Excellence

- Define automation roadmap, framework architecture, and coverage targets

- Build scalable automation frameworks integrated into CI/CD pipelines

- Drive regression automation coverage to measurable targets — own the metric, not just the plan

- Instrument automation effectiveness: flakiness rate, coverage delta per release, mean time to detect

Platform & Infrastructure Testing

Lead quality across Velocis-specific surfaces:

- Kubernetes-based PaaS and control plane workflows

- AI/ML inference services (LLM APIs, model serving endpoints, non-deterministic output validation)

- Multi-tenant isolation, RBAC, and provisioning correctness

- Infrastructure provisioning workflows (IaaS, CloudStack abstraction, GPU allocation)

- API gateway, microservices, and inter-service contract testing

- User portals, dashboards, and operator control planes





- Chaos engineering and fault injection — validate graceful degradation, not just happy paths

- Observability-driven QA: use platform telemetry (metrics, traces, logs) as a first-class testing signal

Quality Metrics & Governance

Own and report quality KPIs to engineering leadership:

- Defect Leakage Rate and Escaped Defects per release

- Regression Defect Density

- Automation Coverage % (with trend, not just snapshot)

- Release Readiness Score

- P95 API Latency Regression Detection

- AI Inference Correctness Drift (output quality trend across model versions)

- MTTR for Production Issues

- Customer-Reported Defect Rate

- Platform Reliability and SLA Adherence

Release Management & Governance

- Define and enforce release quality gates ,hard stops, not suggestions

- Lead defect triage, root cause reviews, and post-mortems

- Participate in Go/No-Go decisions with data-backed recommendations

Customer & Production Quality

- Analyze production incidents, identify systemic quality gaps, and drive preventive action

- Partner with Support and SRE on issue resolution and observability tooling

- Proactively surface quality risks before customers do

AI-Augmented QA (What sets this role apart)

Neysa expects this leader to actively drive AI-native quality practices — not as a future roadmap item, but as part of how the team operates now:

- Use LLMs to generate test cases from PRDs, API specs, and architecture docs

- Build AI-assisted test coverage gap analysis into the QA workflow

- Apply AI-based log anomaly detection and failure clustering to accelerate RCA

- Develop frameworks for testing non-deterministic AI outputs — semantic correctness, regression across model versions, and prompt-response consistency

- Evaluate and adopt AI QA tooling (test generation, visual regression, self-healing locators) where they reduce manual overhead without sacrificing reliability





- Contribute to Neysa's internal thinking on what "quality" means for agentic AI workloads

Required Qualifications

Experience

- 10–14 years of Software QA experience, with depth in distributed or infrastructure products

- Minimum 3–4 years leading QA teams (hiring, mentoring, performance management)

- Experience in SaaS, Cloud Infrastructure, Platform Engineering, or Enterprise Software

- Hands-on in Agile/Scrum environments; comfortable with fast release cadences

Technical Skills — Required

- API Testing: Postman, RestAssured, Swagger/OpenAPI contract testing

- UI Automation: Playwright (preferred), Selenium, or Cypress

- Performance & Load Testing: k6, JMeter, or Locust

- Test Automation Framework Design (Python or Java-based)

- CI/CD Integration: GitHub Actions, Jenkins, or equivalent

- Kubernetes and containerized workloads — not just awareness, hands-on

- Microservices and distributed systems testing

- SQL and database correctness testing

- Git-based workflows and test-as-code practices

Technical Skills — Robust Preference

- AI/ML platform or LLM API testing experience

- GPU infrastructure or ML serving layer exposure

- Chaos engineering tools (LitmusChaos, Gremlin, or equivalent)

- Infrastructure as Code validation (Terraform, Ansible)

- Security testing fundamentals (OWASP, API fuzzing, pen-test concepts)

- Observability tooling: Grafana, Prometheus, distributed tracing

Leadership Skills

- Team building, hiring bar-setting, and mentoring

- Stakeholder management across Engineering, Product, and SRE

- Executive-level quality reporting , can translate defect data into business risk language

- Ability to influence engineering quality practices without direct authority

- Release governance owns the process, not just the checklist

Why This Role

You'll be the QA lead at a company building infrastructure for the AI era , the kind of platform where a test missed in staging can mean GPU time burned, SLA breaches, or a broken tenant boundary for a paying customer. The work is technical, high-stakes, and genuinely novel. You won't be maintaining a legacy test suite. You'll be building quality engineering from the ground up, on a platform that doesn't have many precedents.

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📌 QA Lead - Cloud/AI/Infrastructure (Mumbai)
🏢 Neysa
📍 Mumbai