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David Kidwell
Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps
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About
Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.
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Key Strengths
- Built graph-based RAG system indexing 1,000+ pages of tax regulations for transaction compliance checks
- Validated ML/NLP solutions with offline metrics (precision/recall) plus real-world time-savings measurement post-deployment
- Designed entity extraction + deduplication pipeline using LLM agents and classical NER as a second pipeline
- Used human domain-expert (accountant) labeled held-out set to benchmark entity resolution via IR metrics
- Production-minded Python workflow design using functional programming principles and automation for maintainability
- Scalability approach leveraging cloud horizontal scaling (AWS) and Kubernetes orchestration
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