Key Responsibilities:
Design, develop, and maintain GenAI-powered automation frameworks that span all testing layers: (unit, component, integration, end-to-end) and diffrent testing types (Functional / None functional).
Leverage AI models to automatically generate, optimize, and maintain test cases, scripts, and data for mobile (iOS/Android) and backend (Node.js) platforms.
Integrate GenAI-driven features such as:
Self-healing tests.
Autonomous test scenario generation.
Visual testing and validation.
Natural language-to-test scripting.
Intelligent defect prediction and anomaly detection.
Automated root cause analysis.
Build real-time dashboards into CI/CD pipelines (e.g., Jenkins, GitHub), providing actionable, data-driven quality insights.
Continuously research, prototype, and deploy the latest GenAI advances to maintain technological leadership in automation.
Govern & mentor GenAI-powered automation frameworks practices as the automation frameworks evolve.
Requirements:
Bachelors/Masters in Computer Science (or equivalent).
5+ years of experience as an automation Architect in large-scale, complex environments.
Demonstrable track record of impact, such as:
Architected and scaled at least two automation platforms/framework with measurable improvements (e.g., flaky test rate, coverage, MTTR).
Shipped GenAI-assisted or AI-driven test automation to production CI/CD (not just prototypes).
Mandatory practical experience with AI-Driven Test Automation Tools: Testim, Applitools, Mabl, Functionize, Diffblue, ReTest, Test.AI, Tricentis Tosca, Sauce Labs AutonomIQ.
Proven experience with GenAI patterns (prompt engineering, embeddings, RAG) and integrating AI/ML solutions into infrastructure.
Experience with Agentic AI concepts and tools, including hands-on work with autonomous agent frameworks (e.g., LangChain, AutoGen), multi-agent systems, and the integration of agentic workflows into enterprise infrastructure.
Strong programming skills in Appuim and TypeScript.
Proven experience as a software/test team lead.
Bonus Skills:
Experience deploying GenAI solutions at scale in cloud-native, distributed environments.
Contributions to AI/ML or GenAI open-source projects.
Experience with mobile device farms, cloud-based testing infrastructure, or AI-driven performance/security testing.
Familiarity with MLOps for model lifecycle management in test automation contexts.
