QA Lead/Sr. SDET

StatusNeo•Parsippany-Troy Hills, NJ

About The Position

StatusNeo is a global AI-native transformation firm helping enterprises design, engineer, and govern AI-led systems with trust at the core. We work with global enterprises across BFSI, retail, healthcare, airlines, and platform-driven industries to transform how software is built, operated, and scaled in an AI-first world. Our work is anchored in Authentic AI™ — an approach that treats AI not as a feature or experiment, but as a continuously evolving system that must be engineered with intent, accountability, and governance. At StatusNeo, we don’t just talk about AI transformation. We build it — across engineering platforms, AI-native SDLC, agentic systems, and enterprise operating models.

Requirements

Programming: Python, Java, TypeScript/JavaScript
Automation: Playwright, Selenium, Cypress, Appium, PyTest, REST Assured
API Testing: Postman, REST Assured
CI/CD: Jenkins, GitHub Actions, Azure DevOps
Cloud & Containers: AWS/Azure/GCP, Docker
Test Management: Jira, Azure DevOps, TestRail
Experience using AI-assisted development and testing tools such as GitHub Copilot, Microsoft Copilot, ChatGPT, ACCELQ, Mabl, Testim, or Functionize.
Understanding of Generative AI, LLMs, Prompt Engineering, and AI evaluation frameworks.

Nice To Haves

Experience testing AI-powered applications and machine learning workflows is highly preferred.

Responsibilities

Lead QA and automation teams, providing technical guidance and mentorship.
Define and implement test strategies, quality standards, and governance processes.
Collaborate with Product, Engineering, DevOps, and ML teams to ensure quality throughout the SDLC.
Track and report quality metrics, risks, and release readiness.
Design and develop scalable automation frameworks using Python, Java, or TypeScript.
Build automated test suites for UI, API, backend, integration, and end-to-end testing.
Integrate automation into CI/CD pipelines using Jenkins, GitHub Actions, or Azure DevOps.
Drive Shift-Left Testing and Continuous Quality Engineering practices.
Leverage AI/LLM tools to accelerate test creation, maintenance, and optimization.
Implement AI-powered capabilities such as: Automated test generation, Self-healing scripts, Test data generation, Defect prediction and analysis.
Validate AI/ML applications by testing: Accuracy and relevance, Hallucinations and bias, Model inference and ranking behavior, Adversarial and edge-case scenarios.