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We are looking for an experienced QA Lead – AI Systems to lead the validation and quality assurance of AI-powered products, including LLM/RAG systems, Software as a Medical Device (SaMD), and other AI-driven digital solutions. This is a key leadership role responsible for establishing end-to-end QA frameworks, validation strategies, and compliance processes for AI/ML models across development and deployment pipelines. The ideal candidate will bridge data science, software engineering, and regulatory QA to ensure AI systems meet performance, safety, and compliance standards.
Job Responsibility
Own and drive QA strategy for AI systems across the model, data, and product lifecycle
Lead AI model validation efforts, covering LLM/RAG testing, bias analysis, and performance evaluation
Define and implement AI Evaluation Metrics (accuracy, fairness, drift, explainability) aligned with business and regulatory expectations
Establish frameworks for Explainability Testing (SHAP, LIME, XAI) and ensure interpretability of AI outcomes
Collaborate with Data Science and MLOps teams to validate models within cloud environments (Azure ML, AWS Sagemaker, Vertex AI)
Drive verification and validation (V&V) for AI models and applications under FDA and SaMD compliance frameworks
Ensure test traceability, documentation, and audit readiness in line with ISO 13485, IEC 62304, and ISO 14971
Develop Python-based automation for AI testing, data validation, and model evaluation pipelines
Provide technical leadership to QA teams, ensuring alignment with AI/ML development best practices
Collaborate with cross-functional teams (Product, Data, Regulatory, Engineering) to identify risks, gaps, and opportunities for test optimization
Present validation findings, risks, and recommendations to senior stakeholders and regulatory reviewers
Requirements
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Biomedical Engineering, or related field
8–12 years of total QA experience, with 2–3 years directly in AI/ML or GenAI QA
Proven experience in AI Model Validation, LLM/RAG Testing, and AI Evaluation Metrics
Strong knowledge of MLOps concepts and cloud platforms (Azure ML, AWS Sagemaker, Vertex AI)
Understanding of FDA AI/ML Compliance, SaMD testing, and regulated software QA
Hands-on expertise in Python automation and testing AI/ML pipelines
Excellent documentation and communication skills with ability to produce traceable validation artifacts