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Praveen V
Mid-Level Software Engineer specializing in Generative AI and RAG systems
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About
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.
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Key Strengths
- Built and deployed production RAG NL2SQL system reducing manual analytics ticketing
- Improved retrieval performance via semantic chunking (recall@5 from 0.65 to 0.78 on 200-question test set)
- Strong evaluation discipline: ground-truth test sets, recall@k, error analysis, and real-user validation
- Designs agent workflows with clear responsibilities, guardrails, and end-to-end observability (Langfuse)
- Effective collaboration with non-technical stakeholders; iterated product to deliver exact schema/table outputs, confidence scores, and clarifying questions
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