QA - Agentic SDLC
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Key skills for this role
About the Role
Virtusa is seeking 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) capable of crawling, modeling, and testing applications independently
- Define test intent and constraints rather than rigid, manual test scripts; evaluate how well autonomous agents handle edge cases and non deterministic logic
- Implement active runtime controls and guardrails to ensure AI generated code passes security, compliance, and functional standards before hitting production
- Train agents to analyze failure patterns, identify root causes, and iterate upon test sets without waiting for 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
- Shift from 'test execution' to 'quality engineering strategy,' ensuring continuous testing aligns with rapid development velocity
Full Job Posting
Overview
- An Agentic SDLC QA (Quality Assurance) role focuses on governing AI driven development lifecycles rather than manually executing test scripts.
- Professionals in this space write autonomous testing agents, engineer testing scenarios, and build governance frameworks to validate software built by AI coding tools.
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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