Staff QA Automation Engineer

• 8+ years in QA and test automation, including 3+ years leading people. You've hired, run 1:1s and performance reviews, and mentored QA engineers, not just led a project. • You've built automation frameworks from scratch. You've taken a product from little or no automation to a working framework across API, UI and backend tests, and you can explain the design choices. • Strong coding in Python and JavaScript/TypeScript. You write maintainable test code daily in both: PyTest for the backend, TypeScript for the frontend. • Deep API testing experience. REST, GraphQL and WebSockets, using tools like PyTest, Postman or REST Assured, including contract, negative and auth testing. • Modern end-to-end UI testing. Playwright or Cypress against React/Next.js apps, with reliable, low-flake suites covering critical user journeys. • Backend and database testing. Testing Python/FastAPI services and PostgreSQL (SQLAlchemy), with SQL-based data validation and attention to database performance. • CI/CD integration. You've wired tests into GitHub Actions, Jenkins or GitLab CI, set up deployment quality gates, and worked with Docker/Kubernetes environments. • Performance and load testing. You've designed load, stress and scalability tests with tools like k6, Locust or JMeter, plus front-end checks like Lighthouse. • Security testing. SAST/DAST in the pipeline (e.g. SonarQube, Snyk, OWASP ZAP, Burp Suite), and testing authentication, role-based access and data encryption. • Multi-tenant SaaS experience. You've tested platforms where many customers share infrastructure and know how to prove one tenant can't see another's data. • Compliance-driven testing. You've supported SOC 2, GDPR or HIPAA requirements with test processes and evidence. • Cloud experience. AWS preferred (EC2, ECS, EKS, Lambda); GCP or Azure also welcome. • Quality leadership and process. You've defined QA standards, metrics and KPIs, release and regression procedures, and runbooks, and you report quality clearly to leadership. • Clear communication and collaboration. You work closely with Product on acceptance criteria, Engineering on testability and DevOps on releases, explaining risk to technical and non-technical people alike, in an Agile/Scrum setup. Nice-to-haves • Testing AI/LLM applications. You've tested LLM features or agentic workflows, can handle non-deterministic output, validate prompts and model outputs, and check for bias and fairness. LangChain/LangGraph familiarity is a plus.

Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. We connect to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and apply machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders. We deploy on customer terms — SaaS multi-tenant, SaaS single-tenant, and on-premises — across AWS, Azure, and GCP. Our customers are Fortune 500 finance teams who require data isolation, auditability, and compliance. • 8+ years in QA and test automation, including 3+ years leading people. You've hired, run 1:1s and performance reviews, and mentored QA engineers, not just led a project. • You've built automation frameworks from scratch. You've taken a product from little or no automation to a working framework across API, UI and backend tests, and you can explain the design choices. • Strong coding in Python and JavaScript/TypeScript. You write maintainable test code daily in both: PyTest for the backend, TypeScript for the frontend. • Deep API testing experience. REST, GraphQL and WebSockets, using tools like PyTest, Postman or REST Assured, including contract, negative and auth testing. • Modern end-to-end UI testing. Playwright or Cypress against React/Next.js apps, with reliable, low-flake suites covering critical user journeys. • Backend and database testing. Testing Python/FastAPI services and PostgreSQL (SQLAlchemy), with SQL-based data validation and attention to database performance. • CI/CD integration. You've wired tests into GitHub Actions, Jenkins or GitLab CI, set up deployment quality gates, and worked with Docker/Kubernetes environments. • Performance and load testing. You've designed load, stress and scalability tests with tools like k6, Locust or JMeter, plus front-end checks like Lighthouse. • Security testing. SAST/DAST in the pipeline (e.g. SonarQube, Snyk, OWASP ZAP, Burp Suite), and testing authentication, role-based access and data encryption. • Multi-tenant SaaS experience. You've tested platforms where many customers share infrastructure and know how to prove one tenant can't see another's data. • Compliance-driven testing. You've supported SOC 2, GDPR or HIPAA requirements with test processes and evidence. • Cloud experience. AWS preferred (EC2, ECS, EKS, Lambda); GCP or Azure also welcome. • Quality leadership and process. You've defined QA standards, metrics and KPIs, release and regression procedures, and runbooks, and you report quality clearly to leadership. • Clear communication and collaboration. You work closely with Product on acceptance criteria, Engineering on testability and DevOps on releases, explaining risk to technical and non-technical people alike, in an Agile/Scrum setup. Nice-to-haves • Testing AI/LLM applications. You've tested LLM features or agentic workflows, can handle non-deterministic output, validate prompts and model outputs, and check for bias and fairness. LangChain/LangGraph familiarity is a plus. • FinTech or financial services background. Experience where accuracy, audit trails and reliability around money matter. • Startup experience. You're comfortable building from zero in a fast-moving, ambiguous environment. • Observability and production monitoring. Datadog, CloudWatch, Prometheus or New Relic, including monitoring AI behavior in production. • Chaos engineering and resilience testing. • Test management tools. TestRail, Zephyr or Jira-based test management. • Certifications. ISTQB Advanced (or similar), AWS Certified Developer or Solutions Architect. • Community involvement. Open-source contributions to testing frameworks, technical writing or conference talks.