Develop, and maintain scalable automation frameworks with reusable components using GitHub, GitHub Copilot, UiPath, and programming languages such as Python, JavaScript, or C#.
Build and enhance end-to-end, API, and UI automation suites using modular, data-driven approaches to maximize test coverage, maintainability, and execution efficiency.
Drive continuous improvements in QA processes, automation strategy, test coverage, and application performance while effectively managing competing priorities in a fast-paced Agile environment.
Perform focused manual testing when required, including integration, ETL, and data validation using reporting and analytical tools.
Support application lifecycle management by executing patch validation, upgrade testing, preventative testing, and ongoing maintenance in alignment with engineering best practices and quality standards.
Basic Qualifications and Experience
Bachelor’s degree and 5 to 9 years of Computer Science, IT or related field experience
Must-Have Skills
Strong hands-on experience in test automation using Selenium, Cypress, Playwright, or similar frameworks, with proficiency in Python, JavaScript, or C#.
Proven experience developing, and maintaining scalable automation frameworks and integrating automated testing into CI/CD pipelines using GitHub Actions, Jenkins, or Azure DevOps.
Experience with Robotic Process Automation (RPA) tools such as UiPath for automating business processes and workflows.
Strong expertise in API, UI, integration, and end-to-end testing, along with data validation using SQL and ETL processes.
Experience leveraging AI-assisted development tools such as GitHub Copilot and implementing intelligent test automation capabilities, including self-healing scripts, AI-generated test cases, and predictive defect detection.
Good-to-Have Skills
Knowledge of AI/ML concepts and their application in software testing, monitoring, and quality engineering.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP), including cloud-native testing strategies.
Experience working with relational (SQL) and NoSQL databases, including vector databases used for Large Language Model (LLM) and Generative AI applications.
Exposure to observability and monitoring tools, performance testing, and modern DevOps engineering practices.
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