Job Description
Position Overview: As a Senior SDET, you will play a leadership role in ensuring the quality and reliability of AI-driven solutions within our teams. Your experience in manual and automated testing, combined with expertise in testing AI/Data Science applications, will advance our testing strategies, methodologies, and tools. Beyond individual contributions, you will mentor team members, promote testing excellence, and influence the team to embed a quality-first mindset in the development process for AI and Data Science initiatives.Key Responsibilities:🔹 AI/ML Testing Leadership Provide leadership in developing testing strategies for AI/ML models, data pipelines, and APIs, ensuring output accuracy and consistency. Collaborate with data scientists and developers to validate AI/ML models, focusing on model fairness, accuracy, and scalability. Drive the development of tools and processes for validating AI algorithms and performance metrics. 🔹 Agile Leadership Partner with the scrum team to align testing strategies with sprint and product goals, promoting a quality-first mindset. Advocate for AI-specific testing practices and methodologies, ensuring they are embedded in the development lifecycle. Stay updated with advancements in AI testing tools and technologies to provide thought leadership. 🔹 Advanced Automation & Manual Testing Lead the design, development, and enhancement of automation frameworks tailored for AI, Data Science, and web/API applications. Ensure integration of automated tests into CI/CD pipelines, enabling rapid feedback on AI features and updates. Strategically design manual and exploratory tests to complement automation, particularly for complex AI-driven features. 🔹 Testing Strategy & Expertise Develop comprehensive test strategies that encompass AI model testing, data validation, and regression testing. Mentor junior team members, enhancing the team’s capabilities in AI/ML-specific testing and overall QA competency. 🔹 Defect Management Foster a proactive approach to defect prevention, emphasizing early identification of issues in AI pipelines and workflows. Promote defect tracking and resolution for both traditional software and AI-driven solutions. 🔹 Collaboration & Communication Collaborate closely with developers, data scientists, and product managers to ensure seamless integration of testing in AI projects. Effectively communicate testing strategies, insights, and results to technical and non-technical stakeholders.
