AI Engineer & Software Engineer — Building intelligent systems that ship to production.
I specialize in LLM integration, multi-agent AI, RAG pipelines, and high-throughput Go/Python backend services. I bridge the gap between cutting-edge AI research and reliable, scalable software engineering.
- 🤖 LLM Systems — Production backends with OpenAI & Anthropic APIs, session management, streaming
- 🧠 Multi-Agent AI — Multi-agent architectures with CrewAI, debate mechanisms, real-time SSE
- 📚 RAG Pipelines — PDF ingestion, vector embeddings, semantic search, source citations
- 🔗 MCP Protocol — Custom MCP servers bridging LLMs to gRPC microservices
- ⚙️ Backend Engineering — Go (gRPC, DDD, hexagonal architecture), Python (FastAPI), microservices
- 🗄️ Vector Databases — Qdrant, ChromaDB, pgvector — evaluation & implementation
| Project | Description | Tech |
|---|---|---|
| The Trading Floor | Multi-agent council of 5 AI agents debating stock decisions in real time | CrewAI, FastAPI, Next.js, SSE |
| MCP-gRPC Bridge | Dynamic MCP server auto-discovering gRPC services as LLM-callable tools | Go, MCP, gRPC, Datadog |
| Astro AI Backend | Production LLM assistant backend with hexagonal architecture | Go, gRPC, OpenAI, Anthropic |
| RAG System (MTA AI) | Full-stack RAG with PDF ingestion, vector search & source citations | Go, React, Vector DB |
| Vector DB POC | Benchmark comparison of Qdrant, ChromaDB & pgvector | Go, Docker Compose |
AI/LLM: OpenAI API · Anthropic API · CrewAI · MCP Protocol · RAG · Vector DBs
Backend: Go · Python · FastAPI · gRPC · Protocol Buffers · DDD · Hexagonal Architecture
Data: PostgreSQL · MongoDB · Redis · MQTT · Qdrant · ChromaDB · pgvector
Frontend: Next.js 14 · React · TypeScript · Tailwind CSS
Infra: Docker · Jenkins CI/CD · Datadog · SonarQube
- 📧 Email: syamsulhuda.uul@gmail.com
- 💼 LinkedIn: linkedin.com/in/syamsulhudauul
- 📄 CV: View PDF