AI Engineer — LLM Systems, RAG & Agents · Published IEEE Researcher · Oracle Certified ×2
I build grounded AI systems — retrieval pipelines, LLM agents, and the production software around them.
- AI Automation Engineer at InvisiEdge Marketing — AI-powered automation tools and platforms for enterprise workflows
- Published researcher — first author of an IEEE paper on JurisGPT, a citation-grounded legal assistant for Indian startup and corporate law
- 100+ LLM evaluation tasks at Outlier AI — RLHF workflows, prompt optimization, and code-generation review
- Client work shipped to production — most recently Vectius, a SaaS operations platform with an encrypted credential vault, invoice OCR + LLM autofill, and a natural-language assistant
JurisGPT: A Citation-Grounded Legal Assistant for Indian Startup and Corporate Law — IEEE Live at jurisgpt.me · Code
RAG over a maintained corpus of 47,867 Indian legal documents (Companies Act 2013 + nine further statutes). Hybrid BM25 + lexical retrieval fused with weighted Reciprocal Rank Fusion; every answer carries inline citations, calibrated confidence badges, and limitation statements.
- Hybrid retrieval reaches Recall@5 0.84 / MRR 0.95 on a 120-query human-verified benchmark (Fleiss' κ = 0.81)
- 80% of answers judged citation-faithful, 2.5% severe-hallucination rate
- Negative result with practical value: a generic MS MARCO cross-encoder reranker lowers nDCG@5 by 8.7 points on hybrid (p = 0.0002) and 16.3 on dense retrieval (p = 0.0001) — paired permutation tests over the full benchmark, reproducible byte-for-byte in rag-eval-lab. Retrieval quality in legal RAG is corpus-bound, not retriever-bound
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Vectius · client work
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rag-eval-lab · benchmark
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ragfuse · library
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