AI QE Engineer - Associate 2

Overview

Join PwC's Acceleration Center India as an AI QE Engineer (Associate 2) within the Software and Product Innovation practice, supporting the design, development and testing of software solutions for client engagements.

What you'll do

• Develop and execute automated test scripts for AI-enabled software features and applications
• Create test cases, test protocols and defect reports to validate functionality, usability and performance
• Perform functional, integration, compatibility and user acceptance testing across client solutions
• Apply Selenium and digital tooling to expand test coverage and streamline regression testing
• Review application behavior, log results and document issues for technical teams to address
• Support quality engineering activities across the software development lifecycle for custom application builds
• Analyze test results and identify patterns that inform remediation and process improvement
• Contribute to release readiness activities and post go-live support for client implementations

What you'll need

• At least a Bachelor's degree
• At least 1 year of experience
• Oral and written proficiency in English

Nice to have

• Hands-on use of GenAI/AI tools (two tools are must: GitHub CoPilot, ChatGPT, Cursor, Claude, Gemini) for software testing, including requirements analysis, test scenario identification, test-case generation, test-data generation, automation code generation, execution support and defect analysis
• Prompt Engineering for QE
• AI Assisted Automation Programming / Scripting
• Evaluate AI models for Testing
• AI assisted CI/CD
• AI Application Testing
• RAG Fundamentals
• Jira, Azure DevOps, Xray, Zephyr or equivalent
• Preference for at least one of the following fields of study: Artificial Intelligence and Robotics, Computer and Information Science, Computer Engineering, Graphic Design, Information Technology, Management Information Systems, Mathematics
• Demonstrating AI quality engineering experience across client engagements and delivery cycles
• Leading testing strategy, automation design and defect analysis for AI solutions
• Mentoring teams in quality engineering methods, tools and delivery practices
• Applying machine learning validation, data testing and performance testing
• Translating technical findings into client-ready recommendations and action plans