Quality Assurance Lead- Fort Worth, TX (occasional ...

Quality Assurance Lead Position: Fulltime Location: Fort Worth, TX (occasional office visit) Duration: Long term Job description: • 8-10 years of experience in Quality Engineering / Software Testing, with at least 3-4 years in a lead role. • Own end-to-end QA strategy and test planning for assigned product/domain, covering functional, regression, integration, non-functional, and UAT testing. • Proven experience leading distributed QA/SDET teams in Agile/Scrum environments. • Define and maintain test approaches, risk-based coverage, and entry/exit criteria for each release and environment. • Strong background as a hands-on SDET / Automation Lead (web, mobile, API). • Embed QA activities into backlog refinement, sprint planning, and development workflows (shift-left). • Align test scope with product priorities and release timelines in collaboration with Product and Engineering. Technical & Tooling Skills Automation, Tooling & CI/CD • Strong understanding of test automation across UI (web and mobile) and API layers. • Ensure high-value scenarios are automated and integrated into CI/CD pipelines for fast feedback. • Proficiency with at least one programming language (Java or Python preferred) and experience working with modern automation frameworks (Selenium/Appium/Rest Assured or equivalents). • Review and approve framework design decisions, code quality, and reusability of automation assets. • Hands-on experience with CI/CD tools (e.g., Jenkins, GitHub Actions), version control (Git), and test execution on grids/device farms. • Collaborate with DevOps to define and maintain test environments, smoke suites, and regression packs. • Familiarity with API testing, JSON/XML payloads, and microservices-based architectures. • Drive continuous improvement of automation suites-stability, performance, coverage, and execution time. AI & Advanced QE AI-Enabled Quality Engineering • Advanced GenAI/AI engineering expertise, including: - Experience with RAG (Retrieval-Augmented Generation) architectures. - Understanding of AI-based applications and agentic AI workflows. - Strong Python programming skills and familiarity with libraries like LangChain and related ecosystems. - Experience designing or using QE accelerators and AI-assisted test tools. • Define and own the roadmap for AI adoption in Quality Engineering: RAG-based testing utilities, agentic AI workflows, and AI-driven analytics. • Ability to evaluate AI/QE accelerators and integrate them into existing QA processes and frameworks. • Guide the team on safe and effective use of AI for test design, test data generation, log/defect analysis, and root cause analysis. • Ability to mentor other team members in consuming AI toolchains and interpreting AI outputs. • Partner with architecture and platform teams to ensure AI-based testing utilities are robust, observable, and governed. Process, Governance & Collaboration Team Management & Stakeholder Communication • Strong knowledge of Agile/Scrum, DevOps practices, and continuous testing. • Lead and mentor SDETs and QE Engineers; perform workload distribution, coaching, and performance feedback. • Experience with test management and defect tracking tools (e.g., Jira, XRay or similar, Confluence). • Conduct regular reviews of test scenarios, automation scripts, and test reports to maintain high standards. • Solid understanding of metrics and KPIs for quality (defect density, leakage, automation coverage, cycle time, etc.). • Establish and publish QA dashboards; communicate quality status, risks, and mitigation plans to stakeholders. • Excellent verbal and written communication; able to work effectively with product owners, architects, developers, DevOps, and business stakeholders. • Facilitate defect triage and Root Cause Analysis sessions; ensure corrective and preventive actions are implemented and tracked.

Created: 2026-03-04