Senior QA Engineer (Automation, AI & Performance) (México)

Senior QA Engineer (Automation, AI & Performance) (México)

Your mission

To act as the ultimate gatekeeper of quality and performance. You will engineer automated test suites that validate not just functionality, but elite performance metrics (Lighthouse 95+), security compliance, and the reliability of Generative AI outputs in a complex, headless architecture.

Role Overview

We are looking for a Senior QA Engineer to lead the testing strategy for a modernized, enterprise-grade digital platform. This is a high-technical role requiring deep experience with Automated Testing Frameworks for a Next.js/Node.js stack.

You will go beyond standard "click-testing." You will own the Performance Engineering (ensuring the site meets strict speed SLAs) and Security Validation (running automated vulnerability scans). Uniquely, this role requires testing Generative AI features—verifying that AI search results are accurate and that PII scrubbing middleware is functioning correctly. You will leverage an AI-first workflow (using tools like Cursor and Claude) to generate test scripts and scenarios 10x faster than manual writing.

Key Responsibilities

- Automated Test Engineering: Architect and maintain end-to-end (E2E) test suites using Playwright or Cypress for a Headless CMS and Next.js frontend.
- Performance Benchmarking: Own the site speed metrics. You will run load tests (using K6 or JMeter) and audit Core Web Vitals to ensure the platform achieves a Lighthouse score of 95+ and LCP under 2.5s.
- Security & Compliance Scanning: Execute automated security scans (SAST/DAST using tools like SonarQube or OWASP ZAP) to validate the staging environment before UAT.
- AI & Data Integrity Testing: Validate the Integration Layer to ensure that Personally Identifiable Information (PII) is correctly scrubbed before being sent to external AI APIs .
- UAT Management: Orchestrate the User Acceptance Testing phase, creating the Test Plan, managing the "Sandbox" environment, and triaging reported defects with the client.
- CI/CD Integration:



Integrate automated tests into the GitHub Actions pipeline to prevent regressions during deployment.
- AI-Accelerated Testing: Actively utilize AI tools (Cursor, Claude, Gemini) to auto-generate edge-case test data, write unit tests, and analyze bug logs.

Core Skills and Competencies

- Tech Stack: Proficiency in JavaScript/TypeScript for writing test scripts.
- Automation Frameworks: Expertise in Playwright (preferred) or Cypress for E2E testing.
- Performance Tooling: Mastery of Google Lighthouse, WebPageTest, and load testing tools like K6.
- API Testing: Experience testing REST and GraphQL endpoints (using Postman or automated scripts) to verify data payloads and error handling.
- AI-Assisted Workflow: Proficiency in using AI coding tools (Cursor, Claude CLI) to rapidly scaffold test suites and generate synthetic test data.
- Accessibility: Knowledge of WCAG 2.1 standards and tools like Axe to ensure the site is accessible to all users.

Required Stack & Tooling ProtocolA. Core Automation (The "Hands")

- Primary Framework: Playwright (TypeScript).
- Why: We need the ability to handle multiple tabs/contexts to test "AI Streaming" responses and handle hydration states better than Cypress.
- Secondary Framework: Cypress (Acceptable, but Playwright is preferred for this specific architecture).
- Language: TypeScript / JavaScript.
- Constraint: Scripts must be strongly typed to integrate with the Next.js repo.

B. Performance Engineering (The "Gate")

- Observability: Google Lighthouse (via Lighthouse CI).
- SLA: Must be configured to fail builds if Performance < 95 or LCP > 2.5s.
- Load Testing: K6 or JMeter.




- Mission: Validate the "Insight Search" API under load (e.g., 500 concurrent vector searches).
- Web Vitals: WebPageTest & Chrome User Experience Report (CrUX) analysis.

C. AI & Data Integrity (The "Brain")

- AI Validation: Fuzzy Matching Libraries (e.g., string-similarity or Jest extended matchers).
- Requirement: Ability to write assertions that validate semantic intent rather than exact string matches.
- Security Scanning: OWASP ZAP or SonarQube (DAST/SAST).
- Specific Task: Verifying PII scrubbing middleware (ensuring no credit cards leak to the LLM).

D. The "AI-Augmented" Workflow (The "Speed")

- Coding Assistants: Cursor (IDE) or Claude 3.5 Sonnet.
- Mandate: Candidates must use these tools to scaffold test suites. Manual writing of boilerplate is considered a performance failure.
- Generative Test Data: Using LLMs to generate "Golden Sets" of questions and answers for regression testing.

E. Infrastructure Integration

- CI/CD: GitHub Actions.
- Task: Integrating the "Performance Budget" json file into the PR workflow.
- Accessibility: Axe-core (Automated WCAG 2.1 checks).

Domains of Mastery

- Testing Non-Deterministic AI: The ability to create "Golden Sets" of questions and answers to verify that the AI Search engine is performing accurately and not hallucinating.
- Vital Metrics: Deep understanding of Core Web Vitals (LCP, CLS, INP) and how to debug code that causes performance regressions.
- Security "Shift Left": Running vulnerability scans early in the build process rather than waiting for a final audit.
- Defect Lifecycle: Managing the flow of a bug from "Reported" to "Fixed" to "Verified" during a high-pressure "Hypercare" launch window.

Tipo de puesto: Jornada completa

Sueldo: $35,000.00 - $60,000.00 al mes

Beneficios:

- Aumentos salariales
- Descuento de gimnasio
- Horarios flexibles
- Opción a contrato indefinido

Lugar de trabajo: Empleo remoto

📌 Senior QA Engineer (Automation, AI & Performance) (México)
🏢 Nova Dynamics
📍 México