QE AI Engineer (Python)

Overview

In this QE AI Engineer role, you fuse quality engineering with AI-enabled solutions to accelerate software delivery. You’ll develop reusable QE assets, frameworks, and accelerators that span web, API, and non-functional testing, while embedding quality early in the lifecycle. You’ll apply practical GenAI use cases such as test generation, defect analysis, and quality reporting to support delivery teams. You’ll collaborate with engineers and product teams to build fit-for-purpose QE tooling and mentor teammates, enabling scalable, high-quality software outcomes.

Pay / Benefits

• tailored benefits
• flexible work options
• Learning and Development opportunities
• inclusive workplace
• equal opportunity employer
• global collaboration

Responsibilities

• Design, build, test, and maintain QE solutions across the SDLC
• Develop and enhance reusable QE assets, accelerators, and automation frameworks
• Promote shift-left quality practices throughout development teams
• Build and support automation across web, API, and non-functional testing
• Contribute to AI-enabled QE use cases: test generation, data augmentation, defect analysis, quality metrics and reporting
• Work with structured and unstructured data to support AI/QE solutions
• Evaluate automation/AI effectiveness and recommend improvements
• Monitor progress, identify issues, and help resolve delivery challenges
• Collaborate with developers, product teams, and engineers to deliver fit-for-purpose solutions
• Review code, design, and test solutions; mentor team members in QE/AI practices

Key requirements

• Software engineering, SDET, Quality Engineering, or test automation experience
• Strong hands-on automation development experience
• Experience across full SDLC, not limited to test execution
• Proficiency in Python (must), plus Java or JavaScript/TypeScript or C#
• Experience with web, API, and non-functional testing
• CI/CD pipeline experience for automated tests
• Strong OO programming and modern engineering practices
• Git-based source control and collaborative workflows
• Strong problem-solving and analytical skills
• Good written and verbal communication; ability to work independently and in a team
• AI/GenAI experience in testing/automation contexts (practical exposure)

• Problem-solving
• Analytical mindset
• Effective communication
• Python
• Java
• JavaScript/TypeScript

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