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.
