AI QA Architect
2 days ago
Experience Level: 15+ years in QA, with 3+ years in AI/GenAI solutioning
Role Overview
This role requires a blend of technical depth, AI solution development skills, and architectural vision to drive innovation in Quality Assurance and accelerate intelligent test automation.
- We are seeking an AI QA Architect with a strong background in Quality Engineering and hands-on expertise in applying Artificial Intelligence (AI) and Generative AI (GenAI) across testing workflows.
- The ideal candidate will architect, design, and implement AI-driven QA frameworks, build agent-based GenAI applications, and integrate intelligent automation solutions into enterprise QA ecosystems.
Key Responsibilities
- AI-Driven QA Strategy & Architecture
- Design and implement AI-powered QA frameworks integrating capabilities such as self-healing automation, intelligent defect prediction, test case generation, and root cause analysis.
- Define and execute the AI enablement roadmap for QA practices, leveraging LLMs, ML models, and automation accelerators.
- GenAI Application Development
- Develop Agent-based GenAI applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Enable multi-agent collaboration for test generation, failure triage, and defect clustering.
- AI Application Integration & Automation
- Build and integrate GenAI applications with GitHub Copilot, CI/CD pipelines, and test automation frameworks.
- Implement AI-based copilots to assist test engineers with code suggestions, scenario design, and defect classification.
- Host and deploy AI applications on cloud (Azure/AWS/GCP) or on-premises intranet environments, ensuring security and compliance.
- Hands-On Development & Implementation
- Develop AI/ML components using Python, JavaScript, or Java, integrating APIs from OpenAI, Gemini, Claude, or Azure OpenAI Service.
- Create and fine-tune LLM prompts and workflows for test artifact generation, requirement understanding, and automation script synthesis.
Required Skills & Experience
- Core QA Expertise: 
- 10+ years of QA experience, with strong knowledge of automation frameworks, performance testing, and CI/CD integration. 
- Proven experience architecting or scaling test automation frameworks (Selenium, Playwright, Cypress, etc.).
- AI & GenAI Skills:
- Hands-on experience in AI-based QA solutions self-healing, test generation, or intelligent analytics.
- Experience developing Agent-based GenAI apps using LangChain, LlamaIndex, AutoGen, or CrewAI.
- Strong understanding of prompt engineering, RAG workflows, and LLM model tuning.
- Experience integrating with GitHub Copilot or other AI developer assistants.
- Programming & Tools:
- Proficiency in Python (preferred), Java, or JavaScript for AI and automation development.
- Familiarity with OpenAI, Hugging Face, Gemini, or Anthropic APIs.
- Knowledge of Azure, AWS, or GCP for AI service deployment and monitoring.
- Experience using Docker, Kubernetes, and REST APIs for app deployment.
Preferred Qualifications
- Experience implementing AI-driven QA accelerators or GenAI copilots within enterprise testing environments.
- Knowledge of security, compliance, and governance for AI-enabled testing workflows.
- Contributions to open-source AI or QA projects will be an added advantage.
- Certifications in AI/ML, Cloud (Azure/AWS), or Advanced Test Automation preferred.
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