Engineering Production AI Systems That Work in the Real World.
Hi, I'm Prince Kumar, a B.Tech CSE (AI & ML) student at CMR University, Bengaluru (CGPA 7.98). My engineering mission is simple: bridging the gap between theoretical machine learning models and scalable, multi-tenant software systems.
B.Tech CSE (AI & ML) • CMR University
RAG Node Pro: Solving Multi-Tenant Document AI
Standard RAG prototypes break when handling multi-user document isolation and high latency. I engineered RAG Node Pro to achieve sub-second conversational research with isolated Pinecone vector namespaces.
How it works: Users upload multi-format documents into isolated tenant namespaces in Pinecone. The conversational engine uses LangChain's history-aware retriever to resolve multi-turn query context, invoking Llama 3.1 8B on Groq LPUs for sub-second streaming answers.
Uploads documents and parses them into 1,000-character chunks with 200-char overlaps. Generates 768/1536-dim vector embeddings via Google Generative AI API.
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) docs = text_splitter.split_documents(raw_docs) embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
Practical Systems & Deployed ML
Real-world implementations spanning multi-tenant RAG research tools, automated resume analyzer models, and SEO travel platforms.
RAG Node Pro — Research Assistant
Multi-Tenant RAG Research Assistant Platform
Full-stack, multi-tenant RAG platform letting users upload documents and query them via isolated, namespace-based vector search in Pinecone and sub-second Groq Llama 3.1 8B LPUs.
AI-Powered Resume & Career Fit Analyzer
ML Resume Scorer & Skill Recommender
ML-powered pipeline that parses candidate resumes and scores career fit against target job descriptions, recommending missing skills and role-specific courses.
Global Travel Agency Website
SEO-Optimized Booking Platform
User- and SEO-friendly travel platform engineered for showcasing global trip itineraries with integrated online booking functionality.
Core Capabilities & Production Stack
Architectural tools, agent frameworks, vector databases, and cloud infrastructure used to engineer high-performance systems.
AI/ML, Agents & RAG Systems
Parallel research agents, rebuttal rounds, Judge consensus
Multi-tenant RAG, conversational retrievers, chunking
Groq Llama 3.1 8B, Groq Vision, GenAI embeddings
Namespace isolation, vector similarity search
Strict JSON schema mode, refusal bypass on Groq
Regression, K-Means clustering, SVM classifiers
Neural networks & deep learning fundamentals
Data processing, feature engineering, ML web dashboards
Backend, Cloud & Infrastructure
High-performance backends, Pydantic, memory optimization
Containerized microservices on Render & multi-cloud
Token usage monitoring, latency tracking, cost analysis
Scraping past JS walls, multimodal claim extraction
Cloud infrastructure & serverless deployment basics
Automated pipelines, collaborative version control
Frontend & Computer Science Core
TypeScript, server components, SSE live streaming
Type-safe full-stack application development
Dark glassmorphic design systems & animations
Core computer science fundamentals & problem solving
Experience & Academic Journey
Chronological overview of 3 engineering internships and academic milestones at CMR University.
Work Experience & Internships
Web Developer Intern
Rosanisa Xperiences LLP
- Researched the company's target industry and business goals to inform platform design and content strategy.
- Built and deployed an SEO-friendly travel agency website showcasing trip offerings to a global audience.
Machine Learning Intern
Prodigy InfoTech
- Built a linear regression model on a dataset of 1,000+ records to perform predictive analysis across numeric features.
- Applied K-Means clustering to segment 1,000+ unlabeled data points into distinct behavioral groups.
- Built an SVM-based image classifier trained on a labeled, multi-class image dataset.
Machine Learning Intern
Cognifyz Technologies
- Built a regression model on hundreds of restaurant records to predict ratings using cost, location, and cuisine.
- Developed a content-based recommendation model to suggest restaurants based on user preference features.
Academic Education
B.Tech in CSE (Artificial Intelligence & Machine Learning)
CMR University
Bengaluru, India
Senior Secondary (12th CBSE)
Mothers' International Academy
India
Secondary School (10th CBSE)
Mothers' International Academy
India
Let's Connect & Build AI Systems
Send a direct message below. Submissions deliver straight to my primary inbox (prkr8132@gmail.com).