Quality Assurance Automation Engineer

The candidate will be responsible for building scalable, reusable, and intelligent automation solutions that integrate Python-based automation frameworks with Robot Framework and AI-powered capabilities. This role requires a strong understanding of automation architecture, API and UI testing, AI/LLM integration, CI/CD pipelines, and enterprise application ecosystems.

The ideal candidate should be comfortable translating complex business and technical requirements into reliable, maintainable, and AI-driven automation solutions.

Required Skills and Qualifications

• 8+ years of overall experience in software engineering, test automation, quality engineering, or automation development.
• Strong hands-on expertise in Python programming and Robot Framework , with demonstrated experience combining both technologies in real-world projects.
• Proven experience developing custom Python libraries and integrating them with Robot Framework.
• Strong understanding of object-oriented programming, data structures, exception handling, debugging, and software design principles.
• Experience developing and maintaining reusable automation frameworks.
• Hands-on experience with API automation, REST services, JSON, HTTP protocols, and authentication mechanisms.
• Experience with UI automation using Selenium, Browser Library, or equivalent tools.
• Practical exposure to Generative AI, LLM APIs, prompt engineering, or AI-assisted automation.
• Experience with Git, CI/CD tools, automated test reporting, and build pipeline integration.
• Good understanding of SQL databases, test data management, and system integration.
• Strong analytical, troubleshooting, communication, and problem-solving skills.

Preferred Skills

• Experience with AI frameworks and libraries such as LangChain, LlamaIndex, or comparable tools.
• Exposure to AI agents, tool calling, Retrieval-Augmented Generation (RAG), and workflow orchestration.
• Experience with commercial or open-source LLMs and embedding models.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with Docker, Kubernetes, and distributed automation execution.
• Knowledge of observability, structured logging, monitoring, and automation analytics.
• Experience integrating AI capabilities with enterprise systems, ticketing platforms, or test management tools.
• Familiarity with security practices for AI applications, including prompt injection mitigation and sensitive data protection.