👋 Hello, I'm

Sumaly Bajracharya

Full-Stack Software Engineer & AI/ML Enthusiast

Passionate about building scalable end-to-end web applications with Node.js, Nest.js, GraphQL, React and MongoDB. 2+ years of full-stack experience and currently advancing my expertise in Machine Learning and AI through my Master's program.

Technical Skills

Technologies and tools I used to build scalable applications

Languages

JavaScript

TypeScript

Python

C++

Backend & APIs

Node.js

NestJS

Express.js

GraphQL

Frontend

React

Redux

HTML5

CSS3

Databases

MongoDB

SQL Server

Cloud & DevOps

AWS

Docker

AI & Machine Learning

TensorFlow

PyTorch

Retrieval-Augmented Generation

Large Language Models (LLMs)

Tools

Postman

Swagger

Apollo Studio

Work Experience

My professional journey in software development

EB Pearls Pvt. Ltd.

Software Engineer (Full-stack)

Nov 2022 – Jul 2024

Sydney, Australia (remote)

▸ Developed and delivered production-ready web applications, making architectural decisions on data modeling and API structure by collaborating with cross-functional teams.
▸ Designed and implemented robust RESTful and GraphQL APIs with React and Redux frontend.
▸ Improved application performance using database indexing, aggregation pipelines, and query optimizations.
▸ Implemented role-based access control (RBAC) and authentication workflows to secure access division across various user levels.
▸ Integrated Twilio (OTP/SMS), SendGrid, AWS SES for email, and AWS S3 for storage.
▸ Enhanced code maintainability using TypeScript with centralized error handling.

EB Pearls Pvt. Ltd.

Software Engineer Trainee

Apr 2022 – Nov 2022

Sydney, Australia (remote)

▸ Implemented node-cron scheduled jobs and Mongoose geo-spatial queries in a SAAS application.
▸ Built responsive web applications and admin dashboards with MERN stack and NestJS with GraphQL.
▸ Utilized Postman, Swagger, and Apollo Studio for API development and testing.

Leadership Experience

Entrepreneurial Lead

NSF I-Corps Hub Great Plains

Jan 2025 - Feb 2025 & Jun 2025 – Jul 2025

North Dakota, USA (remote)

▸ Led 3-member teams in two cohorts to explore AI-driven tools in music and legal domains.
▸ Conducted 40+ customer interviews applying lean startup and customer segmentation techniques.
▸ Collaborated with technical and business mentors on ecosystem modeling and value propositions.

Featured Projects

AI, Machine Learning & Full-Stack Development

Data Analysis & Visualization

E-Commerce Product Success Predictor

Aug 2025 – Dec 2025

Developed a prediction system to estimate expected customer ratings (regression) and launch risk level (classification) for Amazon datasets. Compared Random Forest, Linear/Logistic Regression, XGBoost, and LightGBM. Used SHAP to interpret how different factors impact a product's success.

Machine LearningExplainable AI (SHAP)Predictive AnalysisFeature Engineering

Code

Reinforcement Learning

Uncertainty-Aware RL with LLM Guidance

Aug 2025 – Dec 2025

Advanced RL framework with fine-tuned BERT, Proximal Policy Optimization, Monte Carlo dropout and A* based oracle in a MiniGrid environment, achieving 99.20% success rate and 2009.96 reward AUC.

BERTPyTorchPython

Code

Information Storage & Retrieval

Protein Chatbot with FAISS & LLM

Aug 2025 – Dec 2025

RAG-based web application with Sentence Transformer and FAISS for semantic search, powered by Llama3.2 for accurate protein information.

RAGNLPFAISSPythonDocker

Code

Distributed System

Federated Learning in Distributed Systems

Jan 2025 - May 2025

Performed a comparative analysis of federated learning algorithms (FedAvg, FedProx, Cyclic Weight Transfer, Differential Privacy) in a distributed systems environment using the MNIST dataset.

Federated LearningPythonPyTorch

Code

Computer Vision

Comparative Analysis of Object Detection Models in Autonomous Driving

Sept 2024 - Dec 2024

Pre-processed the KITTI dataset and analyzed the trade-offs between YOLOv5 and Faster RCNN for speed vs accuracy. Optimized the loss function with classification loss, bounding box regression, and objectness loss.

Computer VisionDeep LearningPyTorchTensorFlowPython

Code

Machine Learning

Dental Caries Segmentation for Panoramic X-ray Images

Sept 2024 - Dec 2024

CNN-based model for automated caries detection in panoramic X-rays, creating clinical decision support tools. Segmented dental caries using U-Net architecture with ResNet34 encoder for 233 panoramic X-ray images.

Medical Image AnalysisComputer VisionTensorFlowPythonDeep Learning

Code

View All Projects on GitHub