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Nisarg Shah
Junior Software Engineer specializing in data, systems, and AI engineering
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Early-career/new-grad candidate who built TrendScout AI, an evidence-first market intelligence agent that ingests messy news, extracts entities/events, builds a Neo4j knowledge graph, and answers questions via RAG with citations. Achieved ~95% retrieval relevance by combining ChromaDB semantic search with graph-based retrieval and validating outputs through human evaluation and guardrails to prevent hallucinations.
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Key Strengths
- Built an evidence-first market intelligence LLM agent (TrendScout AI) end-to-end: ingestion → extraction → knowledge graph → RAG Q&A with citations
- Treats LLM systems as retrieval/ranking problems; improved retrieval relevance to 95% using hybrid semantic + graph retrieval (ChromaDB + Neo4j)
- Designed multi-step agent orchestration with structured intermediate outputs and traceable state for debuggability
- Implemented guardrails and fallback behavior to avoid hallucinations (returns no-answer when evidence is weak)
- Established human-eval loop (random sampling) to verify faithfulness and citation accuracy; used error patterns to drive iterations
- Built incremental, repeatable, idempotent pipelines with input validation/cleaning for messy unstructured data
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Experience
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Publications
2 publications
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