Apoorva Chavali

“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”

“AI engineer with production experience building a high-accuracy (98%) fraud detection system operating at real-time latency (1–2s) over millions of transactions, using a multi-model pipeline approach to meet performance constraints. Also implemented Airflow-orchestrated workflows (DAGs, retries, alerts) to replace brittle cron scripts and is currently pursuing a master’s project on real-time ASL-to-text conversion.”