Responsibilities: Outcome-Driven Accountability : Embrace and drive a culture of accountability for customer and business outcomes. Develop quality engineering solutions that solve complex problems with valuable outcomes, ensuring high-quality, AI-driven test automations. Technical Leadership and Advocacy : Serve as the quality advocate for products, ensuring high-quality automation coverage, appropriateness, feasibility, and alignment with business and customer goals. Design, develop, and maintain advanced automation frameworks to drive Automation first mindset using advanced techniques including BDD, AI and GenAI technologies to streamline and enhance the testing process. Engineering Craftsmanship : Maintain accountability for the integrity of test design, test automation, their ongoing maintenance and scale, as well as the quality of solutions. Stay hands-on, self-driven, and continuously learn new approaches, tools, techniques, and frameworks. Integrate AI and GenAI tools and techniques into existing testing processes to improve accuracy, efficiency, and coverage of automated tests. Customer-Centric Engineering : Develop lean quality engineering solutions through rapid, inexpensive experimentation to solve customer needs. Engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time. Incremental and Iterative Delivery : Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, delivering lean, supportable, and maintainable solutions. Cross-Functional Collaboration and Integration : Work collaboratively with empowered, cross-functional teams including product management, experience, engineering, and delivery. Integrate diverse perspectives to make well-informed decisions that balance feasibility, viability, usability, and value. Foster a collaborative environment that enhances team synergy and innovation. Advanced Technical Proficiency : Possess basic knowledge of modern quality engineering practices and principles, including Agile methodologies and DevSecOps to deliver daily product deployments using techniques like fully automated in-sprint testing to accept the stories and work products, powered by AI/GenAI, throughout the SDLC lifecycle. Strive to be a role model, leveraging these techniques to optimize solutioning and product delivery. Demonstrate an understanding of the full lifecycle product development, focusing on continuous improvement and learning. Domain Expertise : Quickly acquire domain-specific knowledge relevant to the business or product. Translate business/user needs and UX/UI designs into test automation. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff. Effective Communication and Influence : Exhibit strong communication skills, capable of articulating complex technical concepts clearly and compellingly. Support teammates and product teams through well-structured arguments and trade-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives. Engagement and Collaborative Co-Creation : Engage and collaborate with product engineering teams, including customers as needed. Build and maintain constructive relationships, fostering a culture of co-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions. Qualifications: A bachelors degree in computer science, software engineering, or a related discipline. An advanced degree (e.g., MS) is preferred but not required. Experience is the most relevant factor. 5+ years of experience in quality assurance and test automation including coding the test scripts, with at least 1-2 years focused on AI and GenAI technologies. Hands-on experience with automation-first techniques like BDD and test automation tools like TOSCA, Selenium, Gherkin, Functionize, Testim, AccelQ. Experience with AI/GenAI tools and frameworks (e.g., TensorFlow, PyTorch, OpenAI, Python, etc.). Understanding of machine learning algorithms and their application in test automation. Experience with cloud hyper-scalers like Azure, AWS, and GCP. Good understanding of methodologies & tools like, XP, Lean, SAFe, DevSecOps, ADO, GitHub, SonarQube, etc. Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.
