Description

Architect and lead the adoption of AI-first, autonomous quality engineering strategies, integrating Agentic AI frameworks into the SDLC.

This role is on-site.

Responsibilities

• Define and drive the QE vision, embedding AI-powered testing and autonomous agents into CI/CD pipelines.
• Design scalable quality engineering platforms using Python, Java, and cloud-native architectures for microservices.
• Implement AI-driven solutions including LLM-based test generation, self-healing automation, and intelligent defect prediction.
• Build and deploy autonomous testing agents for orchestration, decision-making, and adaptive learning from historical data.
• Establish governance for AI ethics and model reliability while mentoring teams on AI-led QE adoption.

Required Skills

• 5+ years of experience in advanced test automation (Selenium, Cypress, Playwright, REST Assured).
• Strong coding proficiency in Python, Java, or JavaScript with experience in cloud platforms (Azure, AWS, or GCP).
• Deep knowledge of CI/CD pipelines (Azure DevOps, Jenkins, GitHub Actions) and containerization (Docker, Kubernetes).
• Hands-on experience with LLMs, prompt engineering, and building AI-driven test data management systems.
• Familiarity with Agentic AI frameworks such as LangChain, AutoGen, CrewAI, or Semantic Kernel.
• Expertise in API, performance, and security testing within distributed and microservices architectures.
• Experience building AI-driven dashboards for predictive quality metrics and anomaly detection.

Preferred Skills

• Experience with vector databases, embeddings, and RAG architectures in testing contexts.
• Knowledge of NLP-based validation and visual AI testing tools.

Key Skills

Education

Bachelor's degree