You will get production-ready AI architecture and implementation roadmap
Top Rated
Top Rated
Project details
I combine senior AI architecture judgment with hands-on delivery experience to design systems that work beyond the demo. This project is built for teams that need clear technical direction on LLMs, RAG, copilots, and AI workflows with production readiness, scalability, and real-world constraints in mind.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Anomaly Detection, Conversational AI, Image ProcessingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Microsoft 365 Copilot, PyTorch, TensorFlowAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$3,500
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 9 days |
Number of Revisions | 1 | 1 | 1 |
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 | - |
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Great service, great communication and fair pricing
About Sung-Hyun
Senior AI/ML Engineer | LLM, RAG & Agent Systems | AI Platforms, Cloud
100%
Job Success
Chicago, United States - 6:05 pm local time
I specialize in transforming complex real-world problems into clean, reliable, and deployable AI systems from model design and optimization to cloud deployment and integration.
𝐖𝐡𝐚𝐭 𝐈 𝐃𝐎:
I architect and build compliant AI systems for regulated industries where accuracy, auditability, and data controls matter as much as model performance.
▸ 𝐋𝐋𝐌 & 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Production AI systems with hybrid retrieval, structured grounding, hallucination mitigation, semantic caching, evaluation pipelines, monitoring, and reliability controls.
▸ 𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 & 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧
Agentic and multi-agent workflows for voice AI, productivity AI, AI accessibility using LangGraph, LangChain, MCP-style orchestration, guardrails, fallback logic, and session-aware memory.
▸ 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Production ML pipelines for ranking, classification, OCR / extraction, multimodal workflows, prediction, and model deployment designed for measurable outcomes and maintainable architecture.
▸ 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 & 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞
Computer vision and multimodal systems for OCR, document processing, form extraction, image classification, object detection, and image-to-structured-data pipelines.
▸𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 & 𝐀𝐈 𝐒𝐚𝐚𝐒
Scalable AI platforms and SaaS products with secure APIs, orchestration layers, cloud-native deployment, multi-tenant architecture, RBAC, and product-ready backend systems.
▸ 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐞𝐝 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Compliance-aware AI systems for healthcare, legal, and fintech, including HIPAA, PHI, PII, auditability, deterministic workflows, and risk-controlled deployment patterns.
▸ 𝐀𝐈 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧𝐭𝐨 𝐄𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
LLM, RAG, ML, and computer vision integrated into enterprise workflows, internal tools, legal document systems, healthcare platforms, accessibility-focused products, and SaaS applications through APIs and event-driven architecture.
◆ 𝐅𝐥𝐚𝐠𝐬𝐡𝐢𝐩 𝐰𝐨𝐫𝐤
▸ 𝐒𝐦𝐚𝐫𝐭𝐂𝐢𝐭𝐞 - 𝐋𝐞𝐠𝐚𝐥 𝐀𝐈 / 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞
Built an AI-powered legal document and citation platform designed to extract citations, organize references, and improve legal research workflows.
Business result: Reduced manual citation review overhead to 80% and improved the speed to 60%.
▸ 𝐌𝐌𝐇𝐏 - 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐞𝐝 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐈
Built an AI-powered mental health platform with structured assessments, dashboards, and privacy-sensitive conversational experiences.
Business result: Improved support continuity and reduced friction across mental health engagement workflows.
▸ 𝐀𝐈 𝐃𝐨𝐜 𝐌𝐚𝐤𝐞𝐫 - 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
Built an AI document-generation system for creating structured documents, presentations, spreadsheets, and multimedia content.
Business result: Reduced manual drafting effort, accelerated content workflows, and improved output consistency.
◆ 𝐂𝐨𝐫𝐞 𝐯𝐚𝐥𝐮𝐞 𝐈 𝐛𝐫𝐢𝐧𝐠
✓ Enterprise level AI systems delivered (Microsoft, Bayer, GE Healthcare)
✓ Production AI architecture that holds up under real usage, not just demos
✓ AI systems designed and built with compliance and risk boundaries in mind
✓ Hallucination risk reduced at the system-design level
✓ Agent workflows built for reliability, not experimentation
✓ Machine learning systems that are deployable, monitorable, and aligned with product goals
◆ 𝐓𝐄𝐂𝐇𝐍𝐈𝐂𝐀𝐋 𝐄𝐗𝐏𝐄𝐑𝐓𝐈𝐒𝐄
LLM / Agents: OpenAI · Anthropic (Claude) · LangChain · LangGraph · LlamaIndex · Hugging Face · MCP · AutoGen · Multi-agent orchestration · tool-use agents · human-in-the-loop · evaluation pipelines · hallucination measurement
Retrieval / RAG: Pinecone · Weaviate · hybrid SQL + vector search · Semantic retrieval · context quality scoring · RAG evaluation harnesses calibrated per-domain
ML / Vision (Healthcare-specific): PyTorch · TensorFlow · OpenCV · OCR · DICOM · multimodal pipelines · Clinical-grade model evaluation standards
Backend / APIs: Python · FastAPI · Node.js · REST APIs · Event-driven systems · microservices · FHIR-compatible API design
Infrastructure: AWS · Azure · GCP · Docker · Kubernetes · CI/CD · MLOps · evaluation pipelines · audit-logging infrastructure · HIPAA-compliant data residency configurations
I'd love to help in building your AI products from ground up or fixing your underperforming systems/models.
Need an AI architect/engineer to own production delivery end to end? Send me a message!
Steps for completing your project
After purchasing the project, send requirements so Sung-Hyun can start the project.
Delivery time starts when Sung-Hyun receives requirements from you.
Sung-Hyun works on your project following the steps below.
Revisions may occur after the delivery date.
Review the client’s goals, use case, and existing system context