Open to opportunities

Dorna Gyawali

@dronarajoffical

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Software engineer building scalable web backends and AI-powered retrieval systems.

Nepal

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What I'm looking for

I’m looking for a backend/AI-focused role where I can build low-latency, scalable pipelines, ship reliable features, and grow fast—especially with systems like retrieval, real-time data, and distributed processing.

I’m an ICT student focused on building production-minded software—especially systems that are low-latency, scalable, and measurable. I care about clean architecture, reliable pipelines, and user-facing performance.

In my Quark project, I built an AI RAG system with persistent memory. I designed a Redis + BullMQ worker pipeline to keep APIs consistently low-latency under concurrent load, implemented hybrid memory (Redis + vector store), and improved retrieval accuracy using an embedding + reranking pipeline.

I also build real-time and distributed systems. With SDR-9, I created a browser-based P2P file transfer system (1GB+) using chunked streaming and Socket.io signaling, while offloading background email and notifications to Redis worker queues. With PX47, I developed a distributed media processing pipeline that uses pre-signed S3 uploads and FFmpeg-based multi-stage transformations for scalable async transcoding and waveform/metadata generation.

Beyond projects, I contribute to GreedyBear (IntelOwl ecosystem), working on threat intelligence IOC analysis using Python/Django tooling and ML-supporting observability. I’m excited to apply this same engineering mindset to teams that value impact, reliability, and continuous improvement.

Experience

Work history, roles, and key accomplishments

Quark AI RAG System

Quark

Apr 2026 - Present (2 months)

Built an AI-powered document analysis system with real-time retrieval and long-term memory. Designed a Redis + BullMQ ingestion/embedding pipeline and a hybrid Redis + vector-store memory to keep API latency low under concurrent load.

AWS S3 Supabase React Docker TypeScript Python ElysiaJS Redis BullMQ Qdrant

SDR-9 P2P File Transfer

SDR-9

Mar 2026 - Present (3 months)

Developed a browser-based peer-to-peer file transfer system that reduces server bandwidth by transferring files directly between peers. Implemented chunked streaming for 1GB+ uploads and real-time Socket.io signaling, with Redis BullMQ workers for background email invites and notifications.

React Express WebRTC Socket.IO Redis BullMQ Resend Docker

Open Source Contributor

GreedyBear (IntelOwl Ecosystem)

Jan 2026 - Present (5 months)

Contributed to a threat intelligence pipeline for IOC analysis by adding GeoIP and ASN enrichment to improve data intelligence. Improved CI reliability by restructuring test suites and adding migration tests, and added feature-importance logging for ML-based threat class categorization.

Python Django Django REST Framework Celery Docker Elasticsearch PostgreSQL GeoIP ASN Enrichment Machine Learning

PX47 Distributed Media Pipeline

PX47

Dec 2025 - Present (6 months)

Built a distributed media processing pipeline to asynchronously process large video/audio files. Used pre-signed S3 uploads and a worker-based engine with FFmpeg to perform transcoding, waveform extraction, and metadata generation.

JavaScript Redis BullMQ FFMPEG AWS S3 Prisma MongoDB

Education

Degrees, certifications, and relevant coursework

Tribhuvan University

Bachelor in Information Communication and Technology, Information and Communication Technology

2024 -

Grade: GPA 3.55/4.0 (First Year Average)

Activities and societies: Relevant coursework: Probability & Statistics, Web Technology, DBMS, OS and OOPS. Projects: Quark (AI RAG + persistent memory), SDR-9 (P2P file transfer), PX47 (distributed media pipeline with FFmpeg + S3).

Pursuing a Bachelor in Information Communication and Technology at Tribhuvan University (Sep 2024–Jun 2028). Coursework includes Probability & Statistics, Web Technology, and DBMS, with projects spanning AI RAG, P2P file transfer, and distributed media processing.