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Dhairya Desai
Senior AI/ML Engineer specializing in healthcare NLP and predictive analytics
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ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.
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Key Strengths
- Built NLP pipeline structuring 2M+ physician notes into facts linked to claims/pharmacy for a patient timeline
- Hybrid ML + rules NER with section awareness, abbreviation expansion, negation and temporality handling
- Strong evaluation/quality loop: clinician-labeled gold sets, inter-annotator agreement, per-entity precision/recall tracking
- Active learning workflow with selective abstention and reviewer feedback into retraining
- Production-grade entity resolution design: deterministic + probabilistic linkage with blocking, thresholds, and human review band
- End-to-end traceability via audit tables/data lineage for linkage decisions
- Handled complex industrial time-series/sensor data issues (clock drift, unit mismatch, tag renames, duplicates) using Spark stateful windowing and watermarks
- Search relevance improvements using hybrid sparse+dense retrieval (BM25/TF-IDF + embeddings/FAISS) with cross-encoder reranking
- Reliable data workflow engineering in Python: Airflow orchestration, config-over-code, data contracts, idempotency/restart safety
- Built and deployed production ML/NLP system for hospital readmission-risk prioritization
- Strong data quality engineering for messy clinical data (validation, record-level flagging, manual review paths)
- End-to-end workflow orchestration with Airflow (DAGs, retries, logging, monitoring, alerting)
- Reliability-focused AI development (automated tests for code + data, monitoring, drift detection, alerts)
- Pragmatic model/retrieval selection (classical ML vs transformers/LLMs; lexical vs dense vs hybrid retrieval)
- Effective collaboration with non-technical clinical stakeholders; translated model outputs into usable workflows
- Delivered measurable operational impact (reduced unnecessary readmissions; saved nurses hours weekly)
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