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Pre-final year · Thapar Institute · Open to SWE / AI roles

I build AI systems that hold up outside a notebook.

An LLM security gateway that inspects traffic in both directions, and a fully offline Jetson Nano pipeline running YOLO, OCR and local RAG at the edge — designed, built and shipped end to end, by me.

0.0s
OCR per frame on Jetson, down from 4-6s on CPU
0+ FPS
YOLOv8n via TensorRT on a 4GB Jetson Nano
0%
On-prem inference - no cloud calls, no recurring cost
Finalist
Bharat AI-SoC Student Challenge, Arm C2S

Selected work

Two systems in depth
01

AEGIS - LLM security firewall & enforcement proxy

A bidirectional enforcement gateway that inspects prompts on the way in and model output on the way out - because keyword filters do not stop prompt injection.

FastAPIPostgreSQL + AlembicReact + WebSocketsSolo build
The system

A multi-stage scanner pipeline - Unicode analysis, YARA rules, regex, then ML classifiers via LLM Guard - scores every prompt and every response before it moves on. A YAML policy engine decides block, redact or allow.

My role

Everything: the FastAPI proxy, async SQLAlchemy 2.x persistence and migrations, the scanner pipeline, canary-token leak detection, the policy engine, and the operator console.

How it is measured

An offline eval suite scores the pipeline against labelled attack and benign datasets, reporting precision, recall, F1, FPR and FNR - so a policy change is a number, not a hunch.

Bidirectional inspection - inbound prompts and outbound responsesStores only 80-byte excerpts + SHA hashes, never raw payloads
Request path
Client appAEGIS proxyModel providerOutbound scan + canary checkClient app
Scanner stages
Unicode analysisYARA rulesRegex / PIILLM Guard classifiersYAML policy decision
Every decision
async PostgresWebSocket ops console
Repository
02

Jetson Smart Board Recorder - edge capture with local RAG

Whiteboard notes captured, read and made searchable entirely on-device - no cloud vision API, no per-frame billing, nothing leaving the room.

Jetson Nano 4GBYOLOv8n + TensorRTLlama 3.1 8B + ChromaDBSolo build
The system

A CSI camera feeds GStreamer; a YOLOv8n TensorRT model watches for the gesture or person cue that triggers capture; homography rectifies the board; GPU EasyOCR reads it; SQLite keeps it; ChromaDB and a local Llama 3.1 8B answer questions over it.

My role

Sole contributor - architecture, the multi-threaded vision pipeline, TensorRT export and OCR optimisation, the local RAG and SQLite backend, Docker packaging, and the Flask Mission Control dashboard.

Result

Moving OCR off the CPU took a frame from 4-6s to about 1.2s, while the trigger model holds 30+ FPS in real time on a 4GB board. Search, Markdown notes, PDF export and semantic chat all run offline.

4-6s to 1.2s per OCR frame30+ FPS trigger inferenceZero cloud cost, fully on-prem
Capture pipeline - all on device
CSI cameraGStreamer ingestYOLOv8n TensorRT triggerHomography rectifyGPU EasyOCRSQLite
Retrieval layer
OCR textChromaDB embeddingsLocal Llama 3.1 8BFlask Mission Control
Repository

Open source & recognition

Kubeflow · CNCF · 2025-present

Contributor - issues & discussions

Working through the Trainer and MCP Server SDK surface (#3107, #238, #107) to learn the codebase and roadmap from the inside, focused on pipelines, training and model-serving workflows.

kubeflow
Arm · C2S Programme · 2026

Bharat AI-SoC Student Challenge - national finalist

Selected as a national finalist for a touchless human-interaction system running real-time computer vision on the NVIDIA Jetson Nano.

Touchless HCI for media control

About

I am a pre-final-year AI & ML undergraduate at Thapar Institute of Engineering & Technology. My habit is to take a problem the whole way - data, model, API, deployment - and then measure whether it actually got better.

That has meant a security proxy that has to survive adversarial input, and an edge device that has to hit real-time on 4GB of memory. Both taught me more about systems than about models.

Comfortable across the stack - Python, C++, FastAPI, PostgreSQL, PyTorch, React - and happy anywhere the work is building software that has to run.

Toolkit
Languages
Python · C++ · C · SQL
AI / ML
PyTorch · Transformers · Scikit-Learn · TensorRT · LLM Guard
Backend & data
FastAPI · SQLAlchemy 2.x · PostgreSQL · SQLite · ChromaDB
Edge & infra
Jetson Nano (JetPack) · GStreamer · Docker · Git
Frontend
React · WebSockets · Flask templates
Foundations
DSA · DAA · OOP · Operating systems · Probability & statistics
Certifications
Machine Learning Specialization - Andrew Ng, Coursera2026
Gemini Certification for Students (K12) - Google for Education2025
Beyond the code
Media Head, Paryawaran Welfare Society · Member, Thapar Venture Club · Member, JITO

If you are hiring someone to build the system, not just the model -

I reply to every genuine message, usually within a day. Internships, SWE roles, open-source collaboration - all welcome.