Job Description
It's fun to work in a company where people truly BELIEVE in what they're doing!
Job Description:
Company Overview
Ingram Micro is the business behind the world’s brands, providing more ways to realize the promise of technology. We are on a path to transform Ingram Micro into a Digital Platform Business, based on experience and outcomes. Our strategy is intently focused on three main users (Customer, Associate, Vendor) of our Digital Platform, which is connected via Data and Intelligence. These platforms together are the Ingram Micro Xvantage™
Role Overview
As we scale our Quality Engineering and automation capabilities, the role of an Sr. Principal, Quality Engineering and AI Automation Architect is pivotal in transforming our testing approach from traditional validation to intelligent, predictive, and self-evolving quality engineering. This role will lead the design and implementation of AI-first automation strategies, enabling the organization to move towards autonomous testing ecosystems. The architect will be responsible for building scalable, reusable, and enterprise-grade automation frameworks that leverage AI for smarter test generation, adaptive execution, and proactive defect detection.
A key focus will be to embed AI-driven quality intelligence into CI/CD and DevOps pipelines, ensuring faster release cycles with built-in, data-driven quality gates. By integrating advanced capabilities such as self-healing automation, predictive analytics, and generative AI, this role will significantly enhance test efficiency, coverage, and reliability.
The AI Automation Architect will also play a critical role in shaping future-ready QE practices, driving adoption of emerging technologies such as Generative AI, autonomous agents, and observability-driven testing. This includes influencing engineering teams, establishing standards, and ensuring consistent adoption across programs.
Ultimately, this role is central to reducing manual effort, accelerating delivery timelines, and improving product quality at scale, while optimizing cost and enabling a best-in-class, resilient platform experience for our customers.
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