{ Abhishek Kumar
Singh }
AI-focused backend engineer building production-grade systems in retrieval engineering, fintech, and fraud detection. Experienced in scalable backend architecture, graph-based ML systems, RAG pipelines, and full-stack application development using modern AI and cloud-native technologies.
Projects
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FinRAG
Citation-Enforced Financial Research Assistant
Production-grade financial RAG assistant over SEC EDGAR filings (10-K/10-Q/8-K) built with LangGraph, hybrid BM25 + vector search, and cross-encoder reranking across 5,400+ chunks. Features paragraph-level citations, an evaluation-driven architecture (RAGAS score 0.73), and Redis caching reducing latency from 4s to <200ms.
CodeBase
Repository-Aware RAG Assistant
Repository-scale code ingestion and RAG assistant analyzing GitHub repos across 20+ file types. Built with MERN, Cohere embeddings, and ChromaDB namespace isolation with <3s query speeds. Features a secure real-time architecture using JWT with httpOnly cookies and live embedding progress updates via Socket.IO.
ChainVigil
Cross-Channel Mule Detection System
Graph-based cross-channel fraud detection system across UPI, ATM, web, and mobile transactions. Models a Unified Entity Graph over 500+ accounts using hybrid GraphSAGE + GAT neural networks for real-time mule-ring detection. Implements a privacy-preserving SHA-256 salted sharing layer and 18 API endpoints.
Finvest
Investment Platform Simulator
Containerized full-stack platform with secure JWT authentication, automated database migrations, role-based access, and AI-driven portfolio recommendations.
Vaultrix
Automated Developer Portfolio Generator
Full-stack web application that reduces the time to create a developer portfolio from hours to under 2 minutes by automating the deployment process via GitHub API integration.
Skills
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I have experience working with:
Languages
[5]Frameworks & Tech
[6]Databases
[4]AI / ML
[4]Systems
[4]Tools & Ops
[3]Certifications
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Full Stack Web Development
UdemyIntroduction to Retrieval Augmented Generation
IBMEthical Considerations for Generative AI
IBMAnalyzing and Visualizing Data with Microsoft Power BI
LinkedIn LearningGenerative AI
LinkedIn LearningSQL
LinkedIn LearningResume
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> Resume
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