Quality Engineering Arch Manager

Quality Engineering Arch Manager

Skill required: Tech for Operations - Functional Test Planning
Designation: Quality Engineering Arch Manager
Qualifications:Any Graduation
Years of Experience:13 to 18 years
About Accenture
Accenture is a global professional services company with leading capabilities in digital, cloud and security.Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Technology and Operations services, and Accenture Song— all powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. Our 784,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.Visit us at www.accenture.com
What would you do? • You will be part of the Technology for Operations team that acts as a trusted advisor and partner to Accenture Operations. • The team provides innovative and secure technologies to help clients build an intelligent operating model, driving exceptional results. • We work closely with the sales, offering and delivery teams to identify and build innovative solutions. • We are looking for a hands-on Quality Engineering Manager to lead engineering teams delivering high quality reliable AI-augmented applications across complex cloud-native environments. This is a leadership role for a mature practitioner who is equally comfortable architecting a test strategy coaching senior engineers and standing in front of a client leadership team to make the case for change. • AI is fundamentally ch • Must-have • Deep hands-on experience in functional integration end-to-end and non functional testing across multi system distributed cloud-native applications. • Practical experience applying GenAI to testing LLM-driven test case generation self-healing scripts synthetic test data visual and AI based regression and defect clustering. Comfortable with prompt engineering as a testing skill. • Working knowledge of agentic AI
What are we looking for? • Primary skill - Functional Test Planning - P5 • Secondary skill - Test Management - P5
Roles and Responsibilities: • Lead modern quality engineering delivery • Lead and actively contribute to functional integration end to end performance and resilience testing across distributed cloud-native and AI-enabled systems. • Own the end to end quality strategy for multiple concurrent workstreams from requirements review and test architecture through release readiness and production quality signals. • Stay hands on where it matters most: code review of critical automation root cause analysis of complex defects and design of the hardest test scenarios. • Drive AI-native automation and agentic testing • Champion adoption of GenAI assisted test authoring LLM based test case generation from user stories and acceptance criteria AI-generated synthetic test data visual regression and self-healing UI automation. • Introduce agentic testing patterns autonomous agents that plan generate execute and analyze tests; multi-agent orchestration across the SDLC and clear guardrails for what agents decide versus what humans approve. • Establish human in the loop review gates so AI-generated tests are curated by experienced engineers before they become part of the trusted suite capturing the productivity gains without inheriting hallucinated or low-value coverage. • Modernize automation frameworks (Playwright Cypress Selenium Appium REST-assured Pact k6 and Gatling) and integrate them into CI and CD pipelines with quality gates that support continuous shift left and shift right testing. • Test agentic and GenAI-powered solutions • Define test strategies for AI-native features where behaviour is non-deterministic identifying what to assert how to handle variability across runs and where human judgement must remain in the evaluation loop. • Design test coverage for agentic workflows and multi-step AI interactions including task completion tool invocation accuracy decision branching and failure and fallback behaviour across agent chains. • Establish regression and monitoring approaches