QA Engineer

Skills and Competencies

Strong programming and test automation experience using Python, with hands-on expertise in Robot Framework or similar automation frameworks.

Practical experience testing AI/ML and AI-enabled applications, including model outputs, APIs, backend services, and application integrations.

Strong knowledge of QA methodologies, test automation strategies, API testing, and software quality engineering best practices within Agile/Scrum environments.

Experience with AWS, Microsoft Azure, or Google Cloud Platform (GCP) and testing cloud-native or microservices-based applications.

Experience integrating automated tests into CI/CD pipelines using Jenkins, GitHub Actions, or similar platforms.

Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI-powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related technical discipline, or equivalent professional experience.

Responsibilities

• Design, develop, and maintain scalable automated test frameworks and scripts using Python and Robot Framework.
• Test and validate AI/ML and AI-enabled applications, including model outputs, APIs, backend services, integrations, and end-to-end workflows.
• Define and implement quality engineering strategies for AI-driven and cloud-based solutions, embedding quality throughout the software development lifecycle.
• Collaborate with Engineering, AI/ML, Product, and DevOps teams to identify defects, improve testability, and contribute to code reviews and technical design discussions.
• Integrate automated testing into CI/CD pipelines to improve test coverage, release reliability, and continuous quality validation.
• Explore emerging AI testing techniques, including Generative AI and LLM testing, such as output validation, hallucination testing, prompt testing, accuracy, and regression testing.

About the Team

You will join a collaborative engineering team focused on building reliable, scalable, and innovative technology solutions. Working closely with Engineering, AI/ML, Product, and DevOps teams, you will help embed quality throughout the development lifecycle and ensure the reliability and performance of AI-enabled and cloud-based applications.

The team embraces modern engineering practices, automation, cloud technologies, and artificial intelligence to continuously improve software quality and deliver future-ready solutions.