Ye Marn Aung

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Computer Science student at San Francisco State University with experience designing and delivering scalable, high-performing, and user-friendly web applications from concept to production.

Experienced in Python, Java, React, FastAPI, AWS, and AI/ML, with strong expertise in integrating ML models and AI systems into production full-stack environments.

Adept at creating responsive and accessible user interfaces, optimizing backend performance, and collaborating across teams to deliver reliable user-facing applications.

Focus

  • Engineering

    Reliable systems, APIs, and data pipelines—clarity, observability, and maintainability first.

  • Applied AI

    Production-minded ML: evaluation, iteration, and tight loops with the people who use the output.

  • Data science

    Rigorous analysis and experimentation—turning complex data into decisions teams can act on.

Education

San Francisco State University

Bachelor of Science, Computer Science (Minor in Mathematics)

San Francisco, CA · Expected December 2026

Professional Experience

SWE Teaching Assistant — San Francisco State University

September 2026 – Present

  • Mentor 48 undergraduate students across multiple teams in designing, developing, and deploying full-stack senior capstone projects.
  • Lead hands-on workshops on GitHub, CI/CD, AWS, and Vercel to help teams ship reliable, production-ready applications.
  • Review student code and provide technical feedback on APIs, database integration, authentication, deployment, and software engineering practices.

Full-Stack Engineer — Develop for Good

May 2026 – Present

Tech stack: Firebase / Firestore, React Native (Expo), TypeScript, Zod, Jest, GitHub Actions

  • Collaborated cross-functionally with designers, software engineers, and clients in an Agile environment to transform 30+ Figma designs into reusable React Native components and production-ready screens.
  • Engineered backend for authentication, goal management, and weekly streak services with Firebase for a React Native platform that can support 300+ end users (consumers and staff) following UserTesting validation.
  • Implemented role-based authorization using Firebase Authentication, Firestore Security Rules, Zod validation, and AsyncStorage, preventing unauthorized access while avoiding the need for higher-tier Firebase features.
  • Documented developer-friendly WCAG accessibility guidelines and audited code to follow the guidelines, creating an accessible platform that can be used by screen readers on both Android and iOS.

Project Experience

Recipe Kitchen: Multimodal Reel-to-Recipe Platform

Live siteGitHub

Tech stack: LangGraph, Google Gemini, FastAPI, Python, TypeScript, Supabase, Silero VAD, Apify, Amazon SQS, AWS ECS, Amazon ECR, GitHub Actions, Vercel

  • Built a multimodal recipe extraction pipeline that turns short-form cooking reels into structured ingredient lists and steps by fusing speech (including Burmese), on-screen captions, and visual frame understanding.
  • Orchestrated ingestion and generation with LangGraph, Google Gemini, and Silero VAD, tagging each ingredient and step with its source (audio, caption, or visual) for transparent recipe cards.
  • Shipped a full-stack product with a FastAPI backend, Supabase auth/storage, and a TypeScript frontend, deployed to Vercel.
  • Designed an async jobs + worker system where the API enqueues job IDs on Amazon SQS and ECS workers claim, run, and complete ingest/recipe/audio/visual pipelines without blocking requests.
  • Automated CI/CD with GitHub Actions: Docker images are built and pushed to Amazon ECR, then the API and worker services are deployed to Amazon ECS on successful main-branch builds.

HowToKeepPlantsAlive: AI Plant Recommendation System & Care Platform

GitHub

Tech stack: LangGraph, PyTorch, Python, MongoDB, Gemini, Cohere, DVC, Prefect, MLFlow, Pinecone, Feast, TypeScript

  • Engineered a two-tower recommendation system that recommends plants based on users’ environment and preferences, improving Recall@10 by 6.6× over a semantic similarity baseline.
  • Designed a rule-based simulation framework to generate synthetic plant–user interaction data and benchmark the recommendation model’s ability to learn behavioral patterns, successfully capturing 8 of 10 predefined rules.
  • Developed LangGraph AI agents capable of plant Q&A and comparisons, using a hybrid RAG pipeline of Pinecone vector search and BM25 retrieval over curated botany datasets.

Chico State University Chatbot: DxHub AI Summer Camp at Cal Poly San Luis Obispo

PresentationGitHub

Tech stack: Node.js, AWS Bedrock, AWS Lambda, AWS API Gateway, AWS OpenSearch Serverless, AWS S3, AWS CloudFront

  • Selected as 1 of 100 participants from 1,000+ CSU applicants for the DxHub AI Summer Camp and assigned to a team developing an AI chatbot for Chico State University stakeholders.
  • Engineered a production Retrieval-Augmented Generation (RAG) system within the AWS ecosystem by building a knowledge base from 230+ university and local business webpages, enabling transparent responses with inline citations and source references.
  • Developed a serverless backend using AWS Lambda and API Gateway to orchestrate user requests and perform LLM inference with Amazon Bedrock Claude Sonnet, supporting English/Spanish queries for 500+ daily users.

GaitorGate: Full-Stack Search Engine for AI Tools

GitHub

Team Lead + Backend/Database Engineer

Tech stack: LAMP stack, Flask, AWS EC2, Google Gemini

  • Led a team of 6 over a 4-month period to develop and deploy a production-grade search platform for 200+ AI tools, winning 1st place out of 12 teams in a software engineering competition.
  • Architected a 14-table relational MySQL database and search pipeline supporting ~190 AI tools and 500+ keywords, implementing FULLTEXT and foreign-key indexes for efficient search and data retrieval.
  • Developed Flask API endpoints for authentication, query execution, and user-uploaded tool ingestion, validating system reliability through 20+ student testers.

Board2Board: Chess Utility Tool for Over-the-Board (OTB) Game Recognition

GitHub

Tech stack: Keras, Python, OpenCV, ResNet50, Scikit-learn, Matplotlib, SciPy, scikit-image, TensorFlow

  • Designed a computer vision pipeline with OpenCV to detect, warp, and segment real-life chessboard images into 64 cropped squares for piece classification.
  • Fine-tuned a ResNet50 model on a custom dataset of 2,000+ images, achieving 94% classification accuracy across all classes.
  • Developed an adaptive thresholding system using linear regression to handle variable lighting and image conditions, improving successful board detection rate by 4× across 150 diverse real-world unseen board images.

Skills

Programming languages: Python, Java, JavaScript, TypeScript

Databases: MySQL, MongoDB, PostgreSQL, Pinecone

Cloud/DevOps: AWS, Docker, Railway, Vercel, Google Firebase

ML/AI: Keras, TensorFlow, PyTorch, LangGraph, LangChain, OpenCV, LangSmith

Contact

Ye Marn Aung. Open to software, applied AI, and data science roles. For opportunities or a brief introduction, reach out by email, GitHub, or LinkedIn.

Email · GitHub · LinkedIn