QA - Agentic SDLC
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Key skills for this role
About the Role
Virtusa is looking for a QA professional focused on Agentic SDLC to govern AI-driven development lifecycles. You will design autonomous testing agents, engineer testing scenarios, and build governance frameworks for AI-generated code.
Key Skills for This Role
Responsibilities
- Design, configure, and manage autonomous AI agents (e.g., QA.tech, Test IO, Applitools) for independent application testing
- Define test intent and constraints rather than rigid manual test scripts; evaluate autonomous agent handling of edge cases
- Implement active runtime controls and guardrails to ensure AI generated code passes security, compliance, and functional standards
- Train agents to analyze failure patterns, identify root causes, and iterate on test sets without human intervention
Requirements
- Proven experience in designing QA frameworks and evaluating GenAI/AI accelerators for test case generation and defect management
- Familiarity with modern test automation frameworks (e.g., Playwright, Appium) paired with strong programming or scripting capabilities
- Knowledge of prompt engineering, Retrieval Augmented Generation (RAG), and context management for AI systems
- Strategic vision to shift from test execution to quality engineering strategy
Full Job Posting
Job Description
- Location: Dubai, Dubai, United Arab Emirates
- Date Posted: 02/07/2026
- An Agentic SDLC QA role focuses on governing AI driven development lifecycles rather than manually executing test scripts.
Key Responsibilities
- Agent Orchestration: Design, configure, and manage autonomous AI agents (e.g., QA.tech, Test IO, and Applitools) capable of crawling, modeling, and testing applications independently.
- Goal Directed Testing: Define test intent and constraints rather than rigid, manual test scripts; evaluate how well autonomous agents handle edge cases and non deterministic logic.
- SDLC Governance: Implement active runtime controls and guardrails to ensure AI generated code passes security, compliance, and functional standards before hitting production.
- Failure Analysis & Feedback: Train agents to analyze failure patterns, identify root causes, and iterate upon test sets without waiting for human intervention.
Core Requirements
- AI & Automation Expertise: Proven experience in designing QA frameworks and evaluating GenAI/AI accelerators for test case generation and defect management.
- Technical Skills: Familiarity with modern test automation frameworks (e.g., Playwright, Appium) paired with strong programming or scripting capabilities.
- Understanding of LLMs: Knowledge of prompt engineering, Retrieval Augmented Generation (RAG), and context management for AI systems.
- Strategic Vision: Shift from 'test execution' to 'quality engineering strategy,' ensuring continuous testing aligns with rapid development velocity.
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