About this role
AI Test Engineer
About Xerox Holdings Corporation
For more than 100 years, Xerox has continually redefined the workplace experience. Harnessing our leadership position in office and production print technology, we’ve expanded into software and services to sustainably power the hybrid workplace of today and tomorrow. Today, Xerox is continuing its legacy of innovation to deliver client-centric and digitally-driven technology solutions and meet the needs of today’s global, distributed workforce. From the office to industrial environments, our differentiated business and technology offerings and financial services are essential workplace technology solutions that drive success for our clients. At Xerox, we make work, work. Learn more about us at www.xerox.com.
Role Summary
The AI Test Engineer is responsible for ensuring the quality, reliability, accuracy, and performance of AI-driven applications and machine learning models. This role designs and executes testing strategies, validates AI systems across diverse scenarios, identifies risks and biases, and collaborates with cross-functional teams to deliver high-quality, responsible AI solutions.
Functional / Technical Competencies:
- Software Testing and Quality Assurance methodologies
- AI/ML model testing and validation
- Test case design and test automation
- Performance, scalability, and latency testing
- Python, Java, or similar programming languages
- Testing tools such as Selenium, JUnit, and pytest
- Machine Learning frameworks (TensorFlow, PyTorch, Scikit-learn)
- Data pipeline testing and model evaluation metrics
- Cloud platforms (AWS, Azure, GCP)
- CI/CD pipelines and automation practices
- AI ethics, bias detection, and explainability concepts
- Agile development methodologies
Key Responsibilities
- Develop and execute test plans, test cases, and automation frameworks for AI solutions.
- Validate AI models for accuracy, robustness, fairness, and reliability.
- Perform performance and scalability testing on AI-powered applications.
- Create real-world and synthetic test scenarios.
- Identify defects, edge cases, biases, and risks within AI systems.
- Collaborate with data scientists, developers, QA teams, IT, and business stakeholders.
- Monitor AI systems in production and recommend improvements.
- Ensure compliance with responsible AI and quality standards.
- Document testing outcomes and providing actionable insights.
- Contribute to continuous improvement of testing tools and processes.
Key Outputs / Tangible Results:
- AI test plans and test cases
- Automated test scripts and testing datasets
- AI model validation and performance reports
- Defect and root cause analysis d