Principal Engineer, Web Applications Test Architect

Principal Test Engineer with deep hands-on experience in AI testing

Key Responsibilities:

• Lead the development of comprehensive test strategies and frameworks for AI-powered systems, models, and applications.
• Collaborate with AI research teams, data engineers, and software developers to define test requirements and coverage for AI models and data pipelines.
• Design and implement test cases that validate model accuracy, fairness, robustness, interpretability, and edge-case handling.
• Identify, troubleshoot, and resolve issues related to data quality, model drift, and performance degradation.
• Develop automated testing pipelines that integrate with continuous integration and delivery (CI/CD) environments.
• Ensure that AI systems meet ethical, compliance, and privacy standards through rigorous validation and testing.
• Mentor and coach team members in AI testing methodologies and best practices.
• Evaluate and recommend tools, platforms, and frameworks that enhance AI testing capabilities.

• Stay updated with industry trends, emerging AI technologies, and testing innovations to continuously improve testing stratey

• Drive and take ownership of Stress, Scalability, and Performance (SSP) testing, including planning, execution, defect management, and reporting.
• Lead the design, development, and maintenance of operations dashboards for web applications to ensure performance visibility and actionable insights.
  Qualifications

Qualifications:

• Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 8+ years of experience in software testing, with at least 3 years focused on AI system testing.
• Hands-on experience testing machine learning and AI models, including validation of datasets, algorithms, and model outputs.
• Strong knowledge of AI techniques such as supervised learning, unsupervised learning, neural networks, and reinforcement learning.
• Familiarity with AI frameworks such as PyTorch, scikit-learn, or equivalent.
• Proficiency in scripting languages like Python, R, or others used in AI testing.
• Understanding of metrics for model evaluation (precision, recall, F1 score, AUC, etc.).
• Experience with automation frameworks, test orchestration tools, and cloud platforms (AWS, Azure, GCP).
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications:

• Experience with bias detection and mitigation, fairness testing, and explainability frameworks.
• Knowledge of MLOps and embedding testing within AI deployment pipelines.
• Familiarity with regulatory requirements related to AI systems

Work Environment: