Saeed Emami Nasab
5+ years of production experience building reliable LLM solutions for Web3 and enterprise clients.
About
I’m Saeed Emami Nasab, a Senior AI & Agentic Systems Engineer with 4.5+ years of production experience building reliable LLM solutions for Web3 and enterprise clients. I focus on designing autonomous, agentic workflows (including multi-agent decision loops) and enterprise RAG pipelines that can retrieve accurate answers from large knowledge bases using hybrid dense+sparse search and reranking.
I enjoy bridging deep AI engineering with practical deployment: monitoring LLM endpoints, adding robust fallback strategies, and shipping production-ready services with Python (FastAPI) and modern web stacks. My academic foundation in philosophy, logic, and cognition helps me approach agent behavior and system design with structured reasoning—so the solutions I build are not only powerful, but also dependable and maintainable.
- 5 years' experience
- Joined January 2026
Industry experience
- Software & Internet
- Financial Services
- Education
- Professional Services
- Media & Entertainment
Work experience
Architected and deployed multi-agent autonomous decision loops using LangGraph and Python to automate end-to-end onboarding and content workflows, reducing manual turnaround by 80%. Built enterprise-grade RAG systems over 100k+ technical documents using hybrid vector search (dense+sparse) with reranking, achieving 94%+ retrieval precision. Integrated real-time monitoring and fallback mechanisms to ensure 99.9% uptime for business-critical LLM endpoints.
Architected full-stack web applications and autonomous AI agent workflows. Built responsive Next.js/React platforms integrated with Python FastAPI and Node.js backends. Engineered enterprise RAG pipelines, intelligent bots, and Web3 crypto payment gateways.
Developed full-stack SaaS applications using Next.js/React frontends with FastAPI backends and external AI APIs. Built automated workflow pipelines using n8n to connect CRMs, databases, and LLM APIs for autonomous data synchronization. Containerized AI microservices with Docker and set up reproducible CI/CD pipelines on cloud infrastructure.
Education
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