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.

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