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Pavithra Shankar
Mid-level QA Engineer specializing in AI/ML model validation and data quality
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About
ML practitioner with a QA background who has built end-to-end ML pipelines for a health risk prediction use case (lifestyle + demographics), emphasizing robustness through strict data validation, leakage prevention, and cross-validation. Collaborated with a dietician to sanity-check predictions and refine feature interpretation for real-world practicality; has not yet deployed LLM/AI systems to production and has no hands-on orchestration framework experience but is willing to learn.
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Key Strengths
- Built end-to-end ML pipelines used in real workflows
- Strong focus on preventing data leakage via validation and cross-validation
- Addresses data quality issues (missing values, class imbalance) and model stability
- QA-driven approach to reliability: edge-case testing and failure-focused validation
- Iterative model selection approach; tested multiple models and finalized on XGBoost
- Effective collaboration with non-technical domain expert (dietician) to validate and refine feature usefulness
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