N°1
++ $ whoami --verbose --retrieve

Full-stack developer for production-grade systems and AI applications. This page is a working retrieval-augmented system over my career — it embeds, indexes and answers. Scroll to travel the pipeline, or query it live below.

In production ✓
yes — this page retrieves itself ↷
++
resume.pdf2 pp
Nipun Arora
Full-Stack · Systems
Production Systems
APIs → infra → UI
AI Applications
BERT · NLP · LLMs
Cloud Deployment
AWS · Docker · Nginx
▸ click to open resume.pdf
projects.pdf6 pp
AetherDrop
secure file-sharing · S3
Patient Risk Stratification
ClinicalBERT · ~90% acc
Maitri · TruthGuard
LLaMA · BERT 72k
skills.json1 pp
{
  "languages": ["python",
    "c++", "ts"],
  "backend": ["node",
    "express", "flask"],
  "cloud": ["aws", "docker"],
  "ml": ["bert", "nlp"]
}
notes.md14 pp
## approach
- end-to-end ownership
- idea → deployed product
- real infrastructure

## focus
- backend architecture
- scalable & reliable
INGESTER // feed
Stage01 · CORPUS
Input4 files · 23 pages
Output11,408 tokens

Every career is a document set. Snippets peel off the pages and get pulled into the ingester — and that resume.pdf? Click it.

INGESTER // parse + extract
status ONLINE
tokens 0
aperture CLOSED
encoding: utf-8
§summarychunk_04
Stage02 · INGEST
Parserpypdf · unstructured
Aperturedilates on approach

The parser walks every page and pulls clean text. Keep scrolling — the aperture opens and we go in.

inside the ingester // tokenization + chunking bay
NodeS3 APIBERT FlaskAWS ZIPEC2 NLPDocker
chunk_0006
NodeS3API
512 tok · overlap 64
chunk_0007
BERTNLPFlask
498 tok · overlap 64
chunk_0008
AWSEC2Docker
505 tok · overlap 64
corpus → 0 / 27 chunks
Stage03 · CHUNK
Window512 tokens
Overlap64 (~12%)

Pages are scanned into tokens; the blades group tokens into overlapping chunks — structure stays whole.

chunk
→ vector
[0.213, -0.841, 0.077, 0.455, … +380 more]
projects skills infra
Stage04 · EMBED
ModelMiniLM-L6-v2
Dims384 · cosine

Meaning becomes geometry — each chunk is projected into a space where "similar" literally means "close."

graph HNSW
M 16 · ef 200
edges 0
query ≈ O(log n)
Stage05 · INDEX
StructHNSW graph
QueryO(log n)

Every vector is wired to its nearest neighbours, so queries hop the map in logarithmic time.

"what has he deployed to AWS?"
c_01
c_03
c_020.91
c_04
c_110.87
c_06
c_07
c_100.82
c_08
c_12
c_05
c_13
Stage06 · RETRIEVE
Methodbm25 ⊕ dense · rrf
Top-k3 · reranked

Hybrid search casts a wide net; a reranker re-scores the shortlist. Three chunks survive.

system

Answer using ONLY the retrieved passages. Cite as [n]. Refuse if uncovered.

context
[1] projects.pdf · §aetherdrop
[2] projects.pdf · §risk
[3] notes.md · §approach
user

What has Nipun shipped to production?

assistant

ingestchunkembedindexretrievegenerate
✓ pipeline complete
Stage07 · GENERATE
Groundcontext only
Citeinline [n]

Written against the passages only, every claim cited — with a polite refusal when similarity runs dry.

01 · corpus
depth 0.00 · sector corpus
scroll to travel the pipeline ↓
corpusingestchunkembedindexretrievegenerate
03 · SHIPPED WORK — 6 UNITS

Real systems. Built & deployed.

The pipeline above is a schematic on purpose. These are the full-size units — end-to-end ownership, from idea to working infrastructure.

As-built
1

AetherDrop

secure file-sharing

Secure file-sharing platform with expiring links, download limits and cloud-backed storage. ZIP streaming for multi-file downloads, fronted by a reverse proxy and tunnel.

  • AWS S3 storage
  • Dockerized on EC2
  • Nginx + Cloudflare
  • Node.js
  • Express
  • AWS S3
  • EC2
  • Nginx
  • Docker
Deployed
View project →
live on real EC2 ↑
2

Patient Risk Stratification

clinical AI

AI system predicting hospital readmissions from clinical notes. Fine-tuned ClinicalBERT on MIMIC-III behind a Flask inference API, with a clinician-facing dashboard on a microservice architecture.

  • ~90% accuracy
  • MIMIC-III
  • microservices
  • ClinicalBERT
  • Flask
  • Next.js
  • Python
3

Maitri AI

offline assistant

Offline AI assistant for emotional support in constrained environments. Local LLaMA inference via Ollama, emotion detection with DeepFace + OpenCV, and a sentiment-aware conversational system with session tracking.

  • 100% local
  • emotion-aware
  • Ollama
  • LLaMA
  • Ollama
  • DeepFace
  • OpenCV
4

TruthGuard

NLP engine

NLP-based fake-news detection engine with explainability. BERT trained on 72,000+ articles, confidence scoring, and an API-ready classification engine.

  • 72k+ articles
  • explainable
  • API-ready
  • BERT
  • NLP
  • Python
  • REST API
5

EcoRate

ML regression

ML system predicting sustainability scores from lifestyle data. Regression and ensemble models with careful feature engineering and a results dashboard.

  • R² = 0.71
  • MSE = 0.73
  • ensembles
  • Regression
  • Ensembles
  • Python
6

HeritageVault

blockchain

Blockchain-based system for tracking ownership of cultural assets. Solidity smart contracts power an immutable ownership ledger with fraud-resistant provenance tracking.

  • immutable ledger
  • provenance
  • Solidity
  • Solidity
  • Smart Contracts
  • Web3
04 · LIVE RAG — TEST BENCH

This is a real RAG system.

On first query it loads a neural embedding model (all-MiniLM-L6-v2) entirely in your browser, embeds my career into 384-d vectors, and retrieves by true cosine similarity. It understands intent — so ask simple things ("what's your name?") or specific things ("tell me about AetherDrop") and it answers like a person, not a spec sheet.

in-browser embeddings · intent-grounded answers · zero required api calls
query ›
answer — grounded & cited idle · index cold

The index is cold. Ask something — it warms (loads the model) on first query.

loading model…0%
// run a query to see the retrieval trace
Embedder
cold
Dimensions
Index
0 / 17
Last latency