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Akshit Modi
Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps
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About
Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.
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Key Strengths
- Improved downstream ML performance by raising F1 from 0.81 to 0.92 via EHR standardization and data quality work
- Healthcare NLP experience extracting oncology EHR data from unstructured/mixed sources
- Cost-efficient model fine-tuning using LoRA/QLoRA
- Strong data quality and schema normalization practices using clinical vocabularies (ICD, SNOMED CT, RxNorm) and validation metrics
- Production-oriented AWS ML/ETL orchestration (Glue, Lambda, SageMaker) including event-driven pipelines
- Rigorous AI/agent evaluation approach (KPIs, sandboxing, integration/regression tests, human-in-loop, A/B tests, drift monitoring, rollback)
- Effective collaboration with clinicians using interpretable ML (SHAP), leading to 18% reduction in readmissions and adoption
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