QA Tester || AI Application Testing Exp is a must

Shift-Afternoon shift hours .

Location Noida,Delhi, Bangalore,Pune or Hyderabad

Mode-Hybrid

• AI Application Testing Experience

  :

  Candidates must have hands-on experience with AI application testing

  . This skill is considered crucial for enhancing the team's capabilities.

• Automation Tools and Frameworks

  : Experience with front-end testing tools (Playwright, Cypress, Selenium) is important. The team currently uses Robot Framework for front-end testing and plans to expand coverage to the AI back end.

• Evaluation Frameworks and ADK:

  Familiarity with evaluation frameworks, especially the

  Agent Development Kit (ADK), is

  needed for testing non-deterministic AI outputs. There is also a plan to build a custom framework on top of ADK for agent evaluation.

• Cloud Platform Experience

  : While Google Cloud Platform (GCP) experience is valued, general experience with major cloud providers (AWS, Azure, GCP) is sufficient. Two to three years of cloud platform experience is preferred.

• Microservices and Python Skills

  : Experience in testing microservices architectures and proficiency in Python are beneficial, especially for testing AI back-end components deployed via serverless services like Cloud Run.

About the role

• AI & Prompt Evaluation:

   Design and execute test strategies for LLM-powered applications. This includes validating prompt engineering effectiveness, testing for "hallucinations," and ensuring agents adhere to safety guardrails. 

• Test Automation Frameworks:

  Build and maintain robust automation frameworks (using Python/Pytest or Playwright) to test the full stack: from the React user interface to the FastAPI back-end services. 

• API & Backend Testing:

  Validate server-side logic and data processing accuracy. You will write automated tests for Python/FastAPI endpoints to ensure high performance and correct data handling. 

• Agentic Workflow Validation:

  Verify complex agent behaviors (using Google ADK/A2A SDK) to ensure multi-step reasoning chains and tool usage are executing as expected. 

• Cloud Integration Testing:

  Verify integrations with Google Cloud Platform services (Vertex AI, Firestore, BigQuery) to ensure data integrity and system reliability in a serverless environment. 

• CI/CD Integration:

  Work with DevOps and Full Stack engineers to integrate automated test suites into the CI/CD pipelines, ensuring immediate feedback on code changes. 

• Defect Management:

   Identify, record, document, and track bugs. Work closely with developers to reproduce complex AI edge cases and verify fixes. 

• Be AmbITious:

  This opportunity is not just about what you do today but also about where you can go tomorrow. When you bring your hunger, heart, and harmony to Insight, your potential will be met with continuous opportunities to upskill, earn promotions, and elevate your career.

What were looking for

• Experience:

   A minimum of 3 years of experience as a QA Engineer or SDET, with a focus on testing complex web applications and API integrations. 

• Technical Skills:

• Scripting & Automation:

   Proficient in Python for writing automated test scripts (Pytest experience preferred). 

• Front-End Testing:

   Experience with UI automation tools (e.g., Playwright, Cypress, or Selenium) for React applications. 

• Back-End Testing:

  Strong understanding of REST APIs; experience testing FastAPI applications and validating JSON schemas (Pydantic). 

• AI/ML Exposure:

   Familiarity with Large Language Models (LLMs). Experience testing chat interfaces, validating non-deterministic outputs, or using evaluation frameworks (e.g., RAGAS, LangSmith) is a major plus. 

• Cloud:

   Experience working within Google Cloud Platform (GCP), specifically viewing logs and debugging in Cloud Functions or Cloud Run. 

• Database:

   Basic SQL knowledge (BigQuery) and NoSQL familiarity (Firestore) to validate data persistence.

• Problem-Solving:

  Ability to troubleshoot complex issues where the "bug" might be an AI reasoning error rather than a code crash. 

• Communication:

  Strong ability to articulate risk and quality metrics to product stakeholders and developers.