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Vigneshwaran Moorthi
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and Clinical AI
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Built and productionized a HIPAA-compliant LLM+RAG Clinical AI assistant at Optum, fine-tuning GPT/LLaMA on de-identified patient notes and integrating FAISS/Pinecone for sub-second retrieval; reported to cut diagnosis time by ~20 minutes per case. Experienced in orchestrating ML pipelines (Airflow, AWS Step Functions, Azure Data Factory) and in reliability techniques for LLM systems (grounding, citations, confidence filters, monitoring) while partnering closely with clinicians and compliance teams.
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Key Strengths
- Built and deployed production LLM+RAG clinical assistant in HIPAA-compliant cloud (AWS/GCP) on Kubernetes
- Reduced clinician diagnosis time by ~20 minutes per case
- Strong hallucination-mitigation approach (strict grounding, citations, confidence filtering, safe fallbacks)
- Low-latency retrieval and inference at scale (FAISS/Pinecone, GPU acceleration, autoscaling)
- Orchestrated end-to-end ML/ETL and retraining workflows (Airflow, Step Functions, Azure Data Factory)
- Systematic agent/workflow testing and evaluation (unit tests, edge cases, monitoring; metrics incl. groundedness/latency)
- Effective cross-functional collaboration with clinicians and risk/compliance stakeholders; translates outputs using SHAP/XAI
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