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Neha Nadiminti
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
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Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.
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Key Strengths
- Built and deployed a production RAG platform for clinical documentation summarization
- Strong focus on reliability in LLM systems (hallucination prevention via retrieval grounding)
- Performance optimization mindset (chunking and embedding reuse to reduce latency)
- Healthcare data privacy awareness (PHI protection and access controls)
- End-to-end workflow orchestration experience (Airflow for ETL/ML pipelines)
- Modular agent/workflow design with clear task boundaries and success criteria
- Measurement-driven evaluation (accuracy, latency, error rates) with continuous monitoring
- Uses human-in-the-loop validation to ensure quality in early production
- Effective collaboration with non-technical operations/compliance stakeholders
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