Ingram Micro
Sr. Principal, Quality Engineering Architect
Remote Quality Engineering role with clear candidate location fit.
PostedJun 13, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
India
Role overview
Sr. Principal, Quality Engineering Architect
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Key Job Functions/ Requirements
- Demonstrated expertise in designing and implementing AI-driven agents to optimize and transform the Software Testing Life Cycle (STLC)
- Strong capability to architect intelligent automation solutions that significantly reduce manual effort across test design, execution, and analysis
- Proven experience in building specialized AI agents such as quality analyzers, test script generators, execution orchestrators, and regression optimization engines
- Lead the end-to-end architecture of AI-enabled test automation frameworks, ensuring scalability, reusability, and adaptability
- Design and develop advanced test infrastructure, including test harnesses, mock services, and data simulation frameworks to enable continuous quality validation
- Drive AI infusion into quality engineering practices, continuously researching and adopting emerging technologies within Agile/DevOps environments
- Evaluate, customize, and integrate AI and automation tools with enterprise ecosystems through extensible and scalable solutions
- Collaborate with enterprise QA and engineering teams to drive innovation initiatives, ensuring successful adoption and measurable impact
- Establish and enforce automation coding standards and best practices through reviews, governance, and continuous improvement
- Partner with product and engineering stakeholders to define quality strategies, scope solutions, and design robust validation approaches
- Champion and embed Behavior-Driven Development (BDD) and Test-Driven Development (TDD) practices across teams
- Enable testability and quality by design through refactoring strategies and promoting unit and component-level validations
- Provide strategic oversight on test environments, infrastructure, and release automation across complex multi-system landscapes
- Leverage AI and analytics to derive actionable insights from quality metrics, enabling predictive and risk-based testing decisions
- Drive adoption of observability and monitoring frameworks using logs, metrics, and dashboards to enhance production validation and feedback loops
- Offer technical leadership in performance, scalability, security, and resilience testing within AI-enabled automation ecosystems
- Mentor and upskill teams on AI-driven quality engineering practices, fostering a culture of innovation and continuous learning
Education
- Bachelor’s Degree in Computer Science or equivalent
Minimum Experience
- Minimum of 10 years of experience
- Strong expertise in designing AI-driven test automation architectures for enterprise systems across UI, API, data, and microservices
- Experience in leveraging Generative AI (LLMs) for automated test case generation, test data creation, and defect analysis
- Deep understanding of machine learning concepts including model validation, bias detection, and performance evaluation
- Proven ability to build self-healing and adaptive automation frameworks that minimize maintenance overhead
- Proficiency in programming languages such as Python, Java, and JavaScript for AI and automation development
- Experience integrating AI capabilities into CI/CD pipelines to enable intelligent quality gates and predictive feedback
- Hands-on experience with modern automation tools like Playwright, Selenium, Cypress, REST Assured, and intelligent automation platforms
- Strong exposure to cloud-native architectures and services (Azure, AWS, GCP) including containerization with Docker and Kubernetes
- Capability to design and implement data-driven testing strategies using analytics and historical defect patterns
- Expertise in test data management using AI techniques, including synthetic data generation and dynamic data provisioning
- Experience in testing AI/ML systems, including data pipelines, model outputs, drift detection, and explainability validation
- Ability to define and implement risk-based testing strategies using predictive analytics and AI insights
- Strong understanding of MLOps and DevOps practices, integrating testing into model lifecycle and production monitoring
- Experience in designing observability-driven quality frameworks, using logs, metrics, and traces to improve test coverage
- Proven ability to drive AI-led quality transformation, mentor teams, and introduce innovative automation practices at scale
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