BS or higher degree or equivalent experience in CS/EE/CE plus equivalent with 5+ years QA experience;
Proficient with web-based UI and RESTful APIs validation via code as well as Unix/Linux and Shell/Python programming skills;
Rich experience in test cases development and failure root cause analysis;
Good command of Cloud management systems and Kubernetes / Docker / Podman;
Experience with building and handling CI/CD pipelines;
Hands-on experience working with Large Language Models (LLMs), including prompt engineering, fine-tuning, or integration into QA workflows;
Fine-tuning or training models for QA-specific tasks – adapting LLMs or other models specifically for testing, documentation analysis, or requirement validation;
Good QA sense, including attention to detail, problem-solving, data analysis, quality standards knowledge, time management etc.;
Excellent communicator, fluent written and verbal English;
Good teamwork skills with the ability to work independently; passion to learn new technologies.
Nice to Haves:
Experience building AI systems such as RAG (Retrieval-Augmented Generation) pipelines, MRC (Machine Reading Comprehension) solutions, or AI agents;
Building AI-powered test generation tools – using LLMs to automatically generate test cases, edge cases, or synthetic test data;
Experience working with NVIDIA GPU hardware is a strong plus;
Scalability or performance testing knowledge is a plus;
Experience with data analysis and system monitoring across distributed systems.
What you'll be doing:
Review product requirements and develop test matrix;
Build testing-related documentation, including test plans, test approach, test cases, and bug reports assessing quality and associated risks;
Manage bug lifecycle and co-work with inter-groups to work towards solutions;
Automate manual tests and assist in the architecture, crafting, and implementing test frameworks;
Enhance the existing testing frameworks used in the organization by our engineers, including yourself, for areas such as UIs, REST APIs, process automation, and performance validation;
Build a reliable fast feedback loop by integrating automation testing in CI and discovery pipelines.