The Business of Fashion
Website:
businessoffashion.com
Job details:
Company Description
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands. At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.
Position Overview
Ralph Lauren Consumer Technology is seeking a Senior Test Architect - Quality Engineering to define and scale a modern quality architecture for AI-enabled commerce experiences, digital platforms, and enterprise quality workflows. This is an individual contributor quality engineering role in which one will provide technical leadership across deterministic and non-deterministic systems, including AI-powered capabilities such as Ask Ralph, Outfitting/ Virtual Try-On, personalization, search, recommendations, and assisted engineering workflows. The role will set the direction and implementing testing strategy for AI system testing, automation frameworks, CICD quality gates, observability, and release readiness with shift left quality strategy. The architect will partner with Product, Engineering, DevOps, Security, Data/AI, and vendor teams to ensure high confidence in customer-facing releases while accelerating quality engineering through responsible use of AI.
Essential Duties & Responsibilities
and Responsibilities
- Define, drive and build the end-to-end test architecture and governance model for AI-enabled systems, digital commerce platforms, and composable technology programs across Consumer Technology.
- Evaluate, pilot, and introduce AI-powered testing tools, autonomous testing platforms, observability solutions, and engineering productivity tools aligned to Ralph Lauren architecture, security, and compliance expectations.
- Create AI-aware validation strategies for Ask Ralph, Outfitting, and similar features, covering prompt behavior, answer relevance, groundedness, hallucination risks, bias, drift, data quality, guardrails, latency, and cost and validating visual accuracy, fit recommendations, personalization behavior, and model consistency.
- Define non-functional testing strategy across performance, scalability, reliability, resilience, accessibility, security validation support, observability, SLO/SLI alignment, and production-readiness gates.
- Mentor QE engineers, SDETs, and align with POD teams on quality architecture, AI-assisted quality practices, automation design, test data strategy, and modern engineering discipline.
- Partner with engineering teams to embed quality gates into CI/CD pipelines, including smoke, regression, deployment verification, synthetic monitoring, and release-readiness checks.
- Prepare and present executive-level quality insights, risks, adoption progress, and recommendations to global stakeholders across Product, Engineering, DevOps, Security, and vendor teams.
- Design scalable automation frameworks for UI, API, integration, contract, mobile apps, visual, accessibility, performance, resilience, and AI system testing using modern frameworks such as DeepEval, Ragas, Playwright, Selenium, Appium, RestAssured, PyTest, and equivalent tools.
- Create requirement review practices using AI to identify ambiguity, missing acceptance criteria, edge cases, testability gaps, dependency risks, compliance concerns, and impacted regression areas.
- Lead automated script generation using AI by defining standards, prompt libraries, reusable patterns, review gates, and secure practices to accelerate test creation without compromising maintainability or accuracy.
- Architect auto-healing automation capabilities that can detect locator failures, diagnose root causes, recommend or apply safe locator fixes, and feed learning back into the framework with human approval where required.
- Implement AI-driven defect categorization for automation failures, including flaky test detection, failure clustering, root-cause suggestions, defect versus script issue classification, and feedback loops to code or test maintenance workflows.
- Build quality insights and observability dashboards covering automation health, coverage, code-review insights, release gates, failure trends, environment readiness, defect leakage, and end-to-end product quality signals.
- Establish AI-powered visual validation for web and mobile experiences, including layout drift, brand consistency, localization differences, responsive behavior, and high-risk customer journey checks.
- Drive AI PR review capabilities for automated pull request feedback related to test coverage, locator strategy, automation design, code quality, risk areas, and adherence to framework standards.
- Enable AI-assisted test case generation from requirements, user stories, acceptance criteria, design documents, production analytics, and defect history, with clear review and traceability controls.
Required Experience
Experience, Skills & Knowledge
- Bachelor's or master's degree in computer science, Engineering, Information Technology, or equivalent practical experience.
- 12+ years of progressive experience in quality engineering, test architecture, automation engineering, SDET leadership, or related technical architecture roles.
- Demonstrated 7+ years of experience defining quality architecture, automation strategy, CI/CD quality gates, and test governance across multiple teams or product areas.
- 2 + years of focused experience in automating testing for AI-enabled applications using metrics such as answer relevance, groundedness, hallucination detection, toxicity/safety checks, latency, cost, and regression consistency and built agents for testing.
- Strong understanding of AI/ML and GenAI concepts including LLMs, RAG, embeddings, vector databases, inference behavior, prompt engineering, agentic workflows, and AI evaluation methods.
- Hands-on expertise in automation frameworks and programming languages such as Playwright, Selenium, JavaScript/TypeScript, Python, Java, PyTest, RestAssured, Postman, and API automation frameworks, DeepEval, or equivalent tools.
- Experience integrating automation suites into CI/CD platforms such as Jenkins, Bitbucket Pipelines, GitHub Actions, Azure DevOps, or equivalent tools.
- Strong understanding of observability and reporting tools such as Grafana, Splunk, Datadog, ReportPortal, ELK, or similar platforms.
- Experience with cloud-native, microservices, API-driven, and composable architecture environments.
- Fluency in agile delivery, test strategy creation, defect governance, release readiness, and risk-based testing.
- Strong communication and stakeholder management skills with the ability to influence product, engineering, leadership, and vendor teams without direct authority. Preferred Skills
- Experience building or scaling Quality Engineering Excellence, platform quality standards, or communities of practice.
- Experience with AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Microsoft Copilot, or custom LLM integrations for engineering workflows.
- Experience testing AI-enabled Virtual Try-On or similar feature for computer vision, personalization, or recommendation-based experiences, with the ability to validate visual outputs, model behavior, data quality, accuracy, latency, usability, and production-readiness.
- Experience with LLM evaluation frameworks or similar approaches for automated evaluation, AI-as-a-judge metrics, prompt regression, and monitoring of AI applications.
- Exposure to contract testing, chaos engineering, synthetic monitoring, SLOs/SLIs, error budgets, and advanced reliability engineering practices.
- Exposure to retail, eCommerce, omnichannel, personalization, Outfitting, search, recommendation, or digital customer experience platforms.
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