Sung-Hyun K.

𝐄𝐱𝐩𝐞𝐫𝐭-𝐕𝐞𝐭𝐭𝐞𝐝 AI Engineer with 10+ years of hands-on experience in architecting and building production-grade AI systems including 6 years at Microsoft and GE Healthcare, where I designed and deployed compliant AI systems in enterprise environments, healthcare, and legal. 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!