AI Tester

Role OverviewAs the QA Lead, you will spearhead the quality strategy for our AI-powered platforms, ensuring seamless integration between manual precision and automated efficiency. You will be responsible for defining the testing lifecycle of machine learning models, traditional software components, and the data pipelines that feed them. This role requires a visionary leader who can bridge the gap between standard QA practices and the evolving world of AI/ML testing.Key ResponsibilitiesStrategy & Frameworks: Design and implement a hybrid QA strategy covering end-to-end testing, including UI, API, and AI-specific validation.AI Validation: Establish benchmarks for AI performance, accuracy, and bias detection; monitor model drift and /"hallucination/" rates in production-like environments.Automation Leadership: Oversee the development of scalable automation frameworks (Web, Mobile, and API) to ensure high-speed releases without compromising quality.Collaboration: Partner with Data Scientists, Developers, and Product Managers to define /"quality/" in the context of probabilistic AI outputs.Test Data Management: Manage complex datasets required for training validation and regression testing, ensuring data privacy and compliance.Mentorship: Lead and upskill a team of manual and automation testers, fostering a culture of continuous learning regarding AI trends.Required Skills & Experience7-10 years in Software Quality Assurance, with at least 3 years in a leadership or lead capacity.Proven experience testing AI/ML-integrated applications or LLM-based features.Hands-on experience building automation frameworks from scratch.Strong understanding of the SDLC and STLC in both Agile and DevOps methodologies.Bachelor’s or master’s degree in computer science, Data Science, or a related field.Technical CompetenciesAutomation Tools: Proficiency in Selenium, Playwright, or Cypress for web, and Appium for mobile.Programming: Strong coding skills in Python (preferred for AI/ML) or Java.API Testing: Expertise in Postman, automation libraries like Rest RestAssured, or similar tools.AI/ML Literacy: Understanding of Model Validation, Prompt Engineering testing, and basic knowledge of ML frameworks (e.g., Rags, Langsmith, Deepeval).DevOps Integration: Experience with CI/CD pipelines (Jenkins, GitLab CI, or GitHub Actions) and cloud environments (AWS, Azure, or GCP).Data Querying: Advanced SQL skills for data integrity verification within LLM backends.

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