Senior Test Automation Engineer

CONTEXT

Our client, a major player in its sector, is embarking on a transformative journey to enhance its core operational systems and leverage artificial intelligence to revolutionize its software development lifecycle (SDLC). The "Central Tenant Register/Activation" project is a cornerstone initiative, serving as the digital heart for managing crucial user and service activations across its vast ecosystem. This system is not merely a database; it is the fundamental gateway that enables seamless interaction for thousands, if not millions, of users, customers, or internal stakeholders. Simultaneously, the organization is integrating advanced AI Agentic developments directly into its SDLC, aiming for unprecedented levels of efficiency, intelligence, and predictive quality in how software is built and maintained. This creates a fascinating and complex challenge: how to ensure impeccable quality, performance, and security when the very tools and processes are dynamically evolving with AI at their core. This role is at the epicenter of this dual innovation, safeguarding quality for both foundational, business-critical systems and the intelligent future of software development itself. You will be instrumental in bridging the gap between human expertise and AI capabilities, ensuring flawless functionality and robust performance in a rapidly advancing technological landscape.

ACTIVITIES

• Design, develop, and maintain comprehensive test automation frameworks and scripts for both traditional functional validations and advanced AI Agentic developments.
• Perform deep functional validations on the Central Tenant Register/Activation system, ensuring flawless user onboarding, data integrity, and seamless service provisioning.
• Analyze, adapt, and refine AI-delivered test cases, enhancing their effectiveness, coverage, and precision through expert human oversight.
• Collaborate closely with development, AI engineering, and product teams within an Agile framework, embedding quality early into the SDLC and providing continuous feedback.
• Execute manual testing for complex scenarios, edge cases, and areas where AI automation is still maturing, ensuring no critical paths are overlooked.
• Monitor, control, and continuously improve the performance and reliability of AI Agentic developments and their associated test scripts, focusing on efficiency and accuracy.
• Actively participate in all Agile ceremonies, including sprint planning, daily stand-ups, sprint reviews, and retrospectives, fostering a culture of continuous improvement and shared ownership.
• Document test plans, strategies, test scenarios, and results meticulously, ensuring transparency, knowledge sharing, and adherence to regulatory standards.
• Proactively identify potential risks and bottlenecks in the testing process, proposing innovative solutions and mitigation strategies, particularly in the context of AI integration.

EXPECTED ACHIEVEMENTS

• Establish a robust, scalable, and intelligent test automation suite that significantly reduces regression cycles, improves time-to-market for critical features, and elevates overall product quality.
• Ensure the Central Tenant Register/Activation system operates with zero critical defects, guaranteeing seamless user experiences, operational continuity, and enhanced trust in the platform.
• Drive the successful integration and optimization of AI-generated test cases, transforming the efficiency and breadth of overall test coverage while validating the AI's efficacy.
• Mentor junior testers and contribute significantly to the evolution of the QA team's best practices, particularly in the challenging and exciting realm of AI-driven testing.
• Become a key voice in defining and implementing the future of quality assurance in an AI-first SDLC environment, shaping the organization's approach to digital excellence and innovation.
• Deliver measurable improvements in test execution time, defect detection rates, and overall software release confidence.

ENVIRONMENT

Agile Scrum, AI Agentic Frameworks, Test Automation Tools (e.g., Selenium, Playwright, Cypress, XCUITest, Espresso), CI/CD Pipelines (e.g., Jenkins, GitLab CI), JIRA, Confluence, Git, potentially Python/Java for scripting, Performance Testing Tools (e.g., JMeter, LoadRunner), API Testing Tools (e.g., Postman, SoapUI). Familiarity with cloud platforms (AWS, Azure, GCP) is a plus, as is experience with containerization technologies like Docker and Kubernetes. Exposure to machine learning testing methodologies and MLOps is highly beneficial.