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Vamshi Raoulakola
Mid-level Full-Stack & Data Engineer specializing in cloud-native systems and FinTech
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About
Built and shipped production AI search and RAG features for a university portal, including an embeddings-based semantic search layer and a documentation-grounded assistant with citations and anti-hallucination prompting. Also developed scalable, reliable data pipelines integrating Google Ads/GA4/Meta APIs for automated reporting, with strong focus on evaluation loops and retrieval quality improvements (hybrid search, chunking, query-log driven iteration).
Experience
Education
Key Strengths
- Shipped end-to-end semantic search enhancement (embeddings + vector store + API + UI updates)
- Built production RAG assistant grounded in official documentation with source citation and 'don’t guess' prompting
- Data-driven iteration using user feedback and metrics (increased time on results pages, decreased bounce rate)
- Designed LLM evaluation loop using real query logs; tracked retrieval accuracy and grounding with human review
- Improved system quality by diagnosing retrieval as bottleneck and implementing hybrid retrieval + better chunking
- Built scalable multi-source marketing analytics pipeline (Google Ads, GA4, Meta) with normalization, retries/checkpointing, and incremental pulls
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