The GenAI Test Architect will lead the design, development, and deployment of an AI-driven Quality Assurance (QA) automation strategy across diverse software development teams. This key role will establish a scalable, General AI-powered framework to transform our QA processes, improving testing efficiency, accuracy, and scope across product lines, including Windows, iOS, cloud services, and firmware. The ideal candidate combines deep expertise in QA automation with advanced knowledge of General AI technologies to streamline product development and enhance overall product quality.

Key Responsibilities

1. QA Automation Strategy. Design and implement a scalable GenAI-based QA automation framework that supports various platforms (Windows, iOS, cloud services, firmware) while ensuring seamless integration and scalability.
2. Collaboration. Work closely with data scientists, software development teams, and AI professionals to address their testing needs, ensuring the AI-driven framework enhances the quality of the entire ecosystem.
3. Standardization & Automation. Standardize QA processes and tools across teams by integrating GenAI technologies to minimize manual tasks, identify issues early in the development cycle, and improve product reliability.
4. AI Architecture. Lead the design and selection of AI technologies and tools (open-source and commercial), integrating them into existing cloud, on-premises, or hybrid environments for optimized automation.
5. Tool Selection & Integration. Evaluate and select third-party GenAI QA automation tools to ensure they align with the technical environment and meet the organization’s testing needs.
6. Documentation & Knowledge Sharing. Create and maintain detailed documentation of the QA automation framework, tools, and best practices. Facilitate knowledge sharing across teams to ensure consistent and uniform adoption of GenAI-powered testing methodologies.
7. Team Education & Mentorship. Train and mentor team members on GenAI-based testing techniques, tools, and best practices to elevate the organization’s QA capabilities.
8. Performance Optimization. Continuously assess and optimize the QA automation framework to ensure high levels of efficiency, accuracy, and reliability in testing operations.
9. Cross-Functional Collaboration. Promote cross-functional cooperation between development, QA, and IT teams to ensure product quality is embedded throughout the release cycle.

Required Qualifications

Education

• Bachelor's degree in Computer Science, Engineering, or related fields, with a strong focus on software quality assurance.

Experience

• 5-8 years of experience in QA automation, including designing and executing automation strategies.
• Proven expertise in GenAI technologies and their applications in QA automation.
• Proficiency in testing tools and frameworks across Windows, iOS, cloud services, and firmware environments.

Skills

• Strong understanding of software development life cycles (SDLC), Agile methodologies, and DevOps/MLOps practices.
• Problem-solving creativity in a fast-paced, dynamic environment.
• Exceptional communication and leadership skills to guide cross-functional teams and initiatives.

Preferred Qualifications

Advanced Education

• Master's or advanced degree in Computer Science, Information Systems, or related fields.

Certifications

• Relevant certifications in QA methodologies, Agile, or project management.

Advanced Skills

• Hands-on experience with machine learning models applied in testing scenarios.
• Knowledge of leading QA automation frameworks and industry best practices.
• Expertise in cloud platforms, distributed systems, and AI tools like PyTorch.

Knowledge & Skills

• Experience in software and cloud systems design, development, and testing.
• Leadership experience in designing and deploying test automation frameworks.
• Strong grasp of AI concepts such as NLP, deep learning, and machine learning.
• Proficiency in AI frameworks (e.g., PyTorch) and programming languages (Python, Java, C++).
• Familiarity with distributed computing frameworks and data engineering tools.

Core Competencies

1. Effective Communication. Strong verbal and written communication skills to collaborate with cross-functional teams.
2. Results Orientation. Ability to deliver high-quality results in a fast-paced, dynamic work environment.
3. Learning Agility. Stay updated on the latest advancements in AI and QA technologies.
4. Digital Fluency. Proficiency in AI-driven digital technologies and cloud systems.
5. Customer-Centricity. Focus on delivering solutions that enhance product quality and meet customer expectations.

Impact & Scope

• Lead large, cross-functional teams, influencing testing strategies across multiple divisions.
• Innovate and provide creative solutions to complex problems within established guidelines and policies.

Complexity

Apply cutting-edge AI solutions to solve challenging problems, driving automation and innovation in the QA domain.

Join HP and lead the future of AI-driven QA automation to ensure superior product quality and streamlined development processes.