Sai Vijay Ragav

Developed a Spring Boot ticketing platform integrated with a FastAPI-based AI service running Qwen2.5-VL. The system automatically extracted 8+ metadata fields from ticket submissions, reducing manual entry effort by an estimated 60–70% for support agents across 20+ states.

• Built RAG-based support assistant using PostgreSQL for structured ticket data across 10+ categories, accelerating resolution times for state-level support teams
• Designed the FastAPI integration layer that bridged the Spring Boot monolith with the Qwen2.5-VL vision model for automated field extraction
• Shipped the platform across multiple state deployments, handling diverse workflows and language requirements

Designed and deployed a multimodal RAG pipeline that ingested, parsed, and indexed over 10,000 patent documents using LangChain and Unstructured. The system enriched each document with structured metadata, dramatically improving downstream retrieval accuracy for the legal research team.

• Built a hybrid search architecture on Milvus supporting semantic retrieval across 1M+ embeddings, reducing query response times to under a second
• Designed metadata extraction and enrichment pipelines that transformed unstructured patent filings into queryable, categorized knowledge
• Collaborated with the research team to iterate on chunking strategies and embedding models for domain-specific retrieval quality

Successfully completed a 4-week AI Azure Virtual Internship, a Microsoft initiative implemented by Edunet Foundation in collaboration with AICTE. Built hands-on solutions using Microsoft Azure AI services through guided projects and practical assignments, strengthening knowledge of cloud-based AI and machine learning workflows.

• Built hands-on solutions using Microsoft Azure AI services including cognitive services, ML model deployment, and cloud-based AI workflows
• Completed guided projects covering AI fundamentals, responsible AI practices, and real-world Azure implementation scenarios
• Strengthened foundational knowledge of cloud-based AI infrastructure and machine learning operations on Azure