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Niveditha A
Mid-level AI/ML Engineer specializing in healthcare ML and LLM/RAG systems
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About
AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.
Experience
Education
Key Strengths
- Built and deployed an end-to-end LLM/RAG system on EMR + clinical notes in a healthcare environment
- Strong prompt-engineering approach for long/noisy clinical text (multi-step prompts, structured extraction, citation-based recommendations)
- Designs modular, orchestrated ML/LLM pipelines with Kubeflow/Airflow/MLflow enabling retraining, versioning, and faster iteration
- Establishes reliability via automated evaluation against gold standards, retrieval quality checks, and post-deploy monitoring (including latency)
- Effectively translates AI workflows to non-technical clinical stakeholders to improve usability and trust
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