Ritigya Singh
Open to work0 years experienceComputer Engineering Student
Computer Engineering student specializing in Machine Learning, Django,Generative AI, and Agentic AI.
Standing Rank
Rank Not Available
Developer Badges
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Skills & Technologies
30 skillsWork Experience
Software Engineering Intern
Saarathi Finance
- Engineered a production AI-powered invoice validation system using Django and Landing.ai Vision API, automating document extraction, validation, and intelligent field matching through REST APIs for live fintech workflows.
- Designed PDF and CSV ingestion pipelines with source-of-truth validation, achieving 95%+ extraction accuracy across real financial documents while significantly reducing manual verification effort.
ML Research Intern
Davise Lab, NIT Delhi
- Trained an STI/RTI risk classification model on 10,000+ hygiene survey records, improving prediction accuracy by 20% through deep autoencoder-based feature extraction and cross-validation.
- Applied HDBSCAN and UMAP for dimensionality reduction and cluster visualization, identifying high-risk population segments for public health analysis.
Projects
Financial AI Agent
View Project- Built an agentic AI financial assistant using the Phidata framework capable of autonomous reasoning, intelligent tool selection, and multi-step task execution through LLM-driven workflows.
- Integrated DuckDuckGo Search, YFinance APIs, and web tools using tool calling and function execution to retrieve live financial news, stock data, market trends, and company insights.
YouTube Transcript Chatbot
View Project- Developed a GenAI-powered chatbot that converts YouTube transcripts into searchable vector embeddings and enables natural language interaction using a Retrieval-Augmented Generation (RAG) pipeline.
- Implemented semantic search using FAISS vector databases, Gemini embeddings, and Gemini 2.5 Flash to retrieve contextually relevant information while minimizing hallucinations.
Legal Intelligence Engine
View Project- Built an LLM-powered legal analysis system implementing the IRAC framework with PDF ingestion, semantic search, and vector embedding-based retrieval for legal document question answering.
- Implemented chunking, vector embeddings, semantic retrieval, and Gemini-powered reasoning to generate explainable legal analysis across multi-document legal corpora.
Leaderboard Standings
Leaderboard Position Pending
Global test scores, peer standing percentiles, and algorithm leaderboard ranks are updated dynamically.
Assessment Highlights
Assessments Not Completed
Coding evaluations, system assessment results, and conceptual score badges will appear here after taking a test.
AI Collaboration Score
AI Collaboration Score Pending
Developer coding behavior, assistant cooperation, and AI pair-programming indicators are evaluated during live coding sessions.
Role Compatibility Profile
Role Compatibility Analysis Pending
Custom matching reports, candidate role compatibility percentiles, and core engineer strength profiles are processed once conceptual code screenings are complete.
Achievements
Certification
Generative AI Applications with RAG and LangChain – IBM / Coursera, Grade: 88%
Certification
NVIDIA: Fundamentals of Deep Learning – Coursera, Grade: 100%
Merit Scholarship
Merit-based scholarship from Thapar Institute of Engineering and Technology
About Details
Professional Bio
Final-year Computer Engineering student with industry experience in Djangop,Machine Learning, Generative AI, and Agentic AI. Built production AI applications using LangChain, LangGraph, Phidata, RAG, Vector Databases, and LLM APIs.
B.Tech in Computer Engineering
Thapar Institute of Engineering and Technology (2023 - 2027)