Architected and deployed an enterprise Knowledge Intelligence Platform using Retrieval-Augmented Generation
(RAG), indexing 50K+ internal documents to enable natural language querying and automated knowledge retrieval
across business units.
• Designed and implemented end-to-end Generative AI workflows leveraging Large Language Models (LLMs) for
enterprise automation, semantic search, and contextual Q&A systems.
• Built RAG pipelines using LangChain, FAISS/Pinecone vector databases, and HuggingFace embeddings, improving
contextual response accuracy by 25–35% through optimized chunking, retrieval tuning, and metadata filtering
strategies.
• Developed scalable document ingestion and preprocessing pipelines for unstructured data (PDFs, reports, internal
knowledge bases), enabling semantic indexing and structured summarization.
• Reduced hallucination rates by approximately 18% through retrieval re-ranking, prompt engineering refinements,
and structured output formatting with validation checks.
• Deployed containerized AI services via FastAPI and Docker on AWS EC2, handling 5K+ requests/day with sub-
300ms average retrieval latency and high system reliability.
• Fine-tuned transformer-based models for domain-specific NLP tasks using parameter-efficient tuning techniques
and iterative prompt optimization to improve domain alignment and inference performance.
• Implemented monitoring and evaluation frameworks to measure retrieval quality, generation relevance, and system
stability using automated metrics and human feedback loops.