You will get a full-stack AI-powered product MVP — from idea to deployment
Project details
I build full-stack AI-powered product MVPs from idea to production — solo, end to end. I solo-shipped OutfitLens: 10M+ vector embeddings, custom vision models, Spring Boot backend, Python ML pipeline, AWS infra, and a Chrome extension with Featured badge. One person, every layer. You get a working MVP with clean architecture, AI integration (Claude API, OpenAI, LangChain, RAG, vector search), cloud deployment on AWS or GCP, and code you can maintain and scale. From database schema to AI pipeline to shipped product — I handle the full stack.
AI Algorithms
Convolutional Neural Network, Large Language Model, Multimodal Large Language ModelAI Applications
AI Chatbot, Conversational AI, Image Recognition, Natural Language GenerationAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$1,000
|
Standard
$2,000
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 14 days | 30 days | 45 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | - | - | - |
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
About Sung Jun
AI Engineer | RAG, LLM Integration, Computer Vision | iOS Native Apps
Chungju, South Korea - 7:52 am local time
I run a live AI platform solo: 4.14M vector embeddings, 91% classification accuracy, 2,000+ users in 7 countries, 0.57s search latency (down from 60s). It's live right now — open the OutfitLens project in my Portfolio below for the link and a full video walkthrough.
What I deliver:
AI & Backend
→ LLM / GPT integration: GPT-4o for automated classification, policy analysis, and RAG-style document processing
→ AI chatbots & agents: from prompt design to deployed API
→ Visual & semantic search: SigLIP2 + LoRA fine-tuning, Qdrant vector DB (4.14M embeddings, 768-dim)
→ ML data pipelines: 200K+ product ETL from Rakuten/CJ Affiliate feeds, 16-phase quality system
→ Cloud deployment: GCP Cloud Run (L4 GPU), Docker, FastAPI, CI/CD
iOS & Mobile
→ Published iOS/macOS/watchOS apps: Swift, UIKit, SwiftUI, TCA (The Composable Architecture)
→ Real-time features: SocketIO integration for live communication
→ Full-stack mobile: from UI to backend API to deployment
How I work:
→ Backend engineer since 2021; designed, built, and shipped my own production AI platform in 2024–25
→ Metrics-first: every number above is measured, not estimated
→ Avg response under 4 hours; comfortable with PR reviews, weekly demos, and written docs
→ Overlap hours available for US time zones
Stack: Python, PyTorch, Qdrant, MongoDB, FastAPI, Docker, GCP, Swift, UIKit, SwiftUI
Send me your AI idea or your broken pipeline — I'll reply with a concrete plan and an honest feasibility check within 24 hours.
Steps for completing your project
After purchasing the project, send requirements so Sung Jun can start the project.
Delivery time starts when Sung Jun receives requirements from you.
Sung Jun works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Architecture
I review your idea, understand your goals and constraints, and design the full-stack architecture — selecting the right tech stack, AI models, APIs, database schema, and cloud infrastructure.
Implementation & Testing
I build the full product — backend, AI pipeline, frontend, and cloud deployment. Clean production code, thorough testing, and a working MVP delivered on time.