Abdullah Saeed
Al Mukalla, Yemen
Abdullah Saeed
AI Agent Engineer | LLM, RAG, Automation, Chatbot
Category : Artificial intelligence (AI)
I build production-ready AI systems — not fragile prototypes that break a week after deployment.
Most AI agent projects fail because they require a complex team to synchronize: an AI engineer, a backend developer, a database architect, and a frontend developer. I handle the entire lifecycle solo. By bridging the gap between autonomous AI logic and enterprise infrastructure, I help SaaS founders and businesses launch scalable AI products faster, with zero integration friction and lower operational overhead.
🔹 What I Build
- Autonomous AI Agents & Workflows: Multi-step architectures using advanced state management, tool-calling capabilities, short/long-term memory handling, and human-in-the-loop validation.
- Production-Grade RAG Systems: Citation-backed, high-accuracy retrieval engines leveraging semantic search, vector databases, and advanced re-ranking to completely eliminate hallucination.
- Full-Stack AI Products: Scalable web applications featuring responsive user interfaces, secure authentication, robust database layers, and background worker queues.
- AI Workflow Automation: Integrating LLMs into existing tech stacks (CRMs, databases, communication channels) to automate high-volume manual operations reliably.
🔹 Real-World Impact
AI Agent Virtual Assistant
Designed and developed a stateful Telegram AI assistant that fully automated customer inquiry handling and schedule management for barbershop business.
- Reduced operational costs by approximately 90.2% compared to hiring a virtual assistant.
- Delivered an end-to-end production architecture: agent logic, FastAPI server, and a secure database layer.
AI Database Analyzer
Built an AI-powered analytics workflow letting the non-technical teams query databases using natural language.
- Eliminating SQL bottlenecks and reducing report turnaround from days to seconds.
- Deployed local LLMs through Ollama to ensure full data privacy while reducing inference costs for enterprise environments.
🛠️ Tech Stack
- AI Architecture & Orchestration: LangGraph, LangChain, RAG Pipelines
- Vector & Relational Databases: PostgreSQL, ChromaDB
- Backend & Infrastructure: FastAPI, RESTful APIs, Node.js, Docker
- Frontend & Systems: Next.js, TypeScript, JavaScript
Most AI agent projects fail because they require a complex team to synchronize: an AI engineer, a backend developer, a database architect, and a frontend developer. I handle the entire lifecycle solo. By bridging the gap between autonomous AI logic and enterprise infrastructure, I help SaaS founders and businesses launch scalable AI products faster, with zero integration friction and lower operational overhead.
🔹 What I Build
- Autonomous AI Agents & Workflows: Multi-step architectures using advanced state management, tool-calling capabilities, short/long-term memory handling, and human-in-the-loop validation.
- Production-Grade RAG Systems: Citation-backed, high-accuracy retrieval engines leveraging semantic search, vector databases, and advanced re-ranking to completely eliminate hallucination.
- Full-Stack AI Products: Scalable web applications featuring responsive user interfaces, secure authentication, robust database layers, and background worker queues.
- AI Workflow Automation: Integrating LLMs into existing tech stacks (CRMs, databases, communication channels) to automate high-volume manual operations reliably.
🔹 Real-World Impact
AI Agent Virtual Assistant
Designed and developed a stateful Telegram AI assistant that fully automated customer inquiry handling and schedule management for barbershop business.
- Reduced operational costs by approximately 90.2% compared to hiring a virtual assistant.
- Delivered an end-to-end production architecture: agent logic, FastAPI server, and a secure database layer.
AI Database Analyzer
Built an AI-powered analytics workflow letting the non-technical teams query databases using natural language.
- Eliminating SQL bottlenecks and reducing report turnaround from days to seconds.
- Deployed local LLMs through Ollama to ensure full data privacy while reducing inference costs for enterprise environments.
🛠️ Tech Stack
- AI Architecture & Orchestration: LangGraph, LangChain, RAG Pipelines
- Vector & Relational Databases: PostgreSQL, ChromaDB
- Backend & Infrastructure: FastAPI, RESTful APIs, Node.js, Docker
- Frontend & Systems: Next.js, TypeScript, JavaScript
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
Built an AI agent for Telegram that automates customer support and appointment booking for barbershop, reducing operational costs by approximately 90.2% compared to hiring a virtual assistant.
Core System Architecture:
- RAG-based AI Assistant: Implemented a Retrieval-Augmented Generation (RAG) system using a knowledge base to provide accurate responses about services, pricing, and booking information to reduce LLM hallucinations.
- Smart Scheduling System: Built a scheduling tool that checks barber availability in real time and returns available booking slots based on selected dates.
- AI Booking Automation: Built an appointment workflow that generates bookings automatically, reducing manual input and improving customer experience.
Production Engineering & Integration:
- Conversational AI Automation: Designed the assistant to handle customer inquiries instantly through Telegram, improving response time and increasing customer engagement.
- Cost & Revenue Optimization: Reduced support costs by 90.2% through automation and contributed to an estimated increase of 10–20 bookings per month.
- Dashboard Integration: Integrated the system with a responsive admin dashboard, built with NEXT.js for managing appointments, tracking business statistics, and monitoring barber schedules.
Core System Architecture:
- RAG-based AI Assistant: Implemented a Retrieval-Augmented Generation (RAG) system using a knowledge base to provide accurate responses about services, pricing, and booking information to reduce LLM hallucinations.
- Smart Scheduling System: Built a scheduling tool that checks barber availability in real time and returns available booking slots based on selected dates.
- AI Booking Automation: Built an appointment workflow that generates bookings automatically, reducing manual input and improving customer experience.
Production Engineering & Integration:
- Conversational AI Automation: Designed the assistant to handle customer inquiries instantly through Telegram, improving response time and increasing customer engagement.
- Cost & Revenue Optimization: Reduced support costs by 90.2% through automation and contributed to an estimated increase of 10–20 bookings per month.
- Dashboard Integration: Integrated the system with a responsive admin dashboard, built with NEXT.js for managing appointments, tracking business statistics, and monitoring barber schedules.
Built an AI-powered analytics workflow letting the non-technical teams query databases using natural language, eliminating SQL bottlenecks and reducing report turnaround from days to seconds.
Core System Architecture:
- Natural Language to SQL Layer: Built a text-to-SQL pipeline using `Qwen2.5-Coder` to convert user prompts into optimized SQL queries for relational databases.
- Database Execution Layer: Developed a query execution system that securely runs generated SQL queries and retrieves structured data in real time.
- AI Data Analysis Pipeline: Implemented a secondary LLM analysis layer using `Qwen2.5 1.5B` to transform raw query results into human-readable business insights.
Production Engineering & Integration:
- Privacy-Focused AI Infrastructure: Deployed local LLMs through Ollama to ensure full data privacy while reducing inference costs for enterprise environments.
- Conversational Analytics Interface: Built a ChatGPT-style interface that enables non-technical users to interact with databases naturally and review previous AI conversations.
Core System Architecture:
- Natural Language to SQL Layer: Built a text-to-SQL pipeline using `Qwen2.5-Coder` to convert user prompts into optimized SQL queries for relational databases.
- Database Execution Layer: Developed a query execution system that securely runs generated SQL queries and retrieves structured data in real time.
- AI Data Analysis Pipeline: Implemented a secondary LLM analysis layer using `Qwen2.5 1.5B` to transform raw query results into human-readable business insights.
Production Engineering & Integration:
- Privacy-Focused AI Infrastructure: Deployed local LLMs through Ollama to ensure full data privacy while reducing inference costs for enterprise environments.
- Conversational Analytics Interface: Built a ChatGPT-style interface that enables non-technical users to interact with databases naturally and review previous AI conversations.
Relevant Coursework: Data Structures, Design & Analysis of Algorithms, and Web system
- PostgreSQL for Everybody on Coursera | University of Michigan 31/10/2023
![PostgreSQL for Everybody on Coursera | University of Michigan]()
I strengthened my understanding of database management and system design, and how use PostgreSQL database efficiently to store and access to data using SQL - Python for Everybody on Coursera | University of Michigan 14/08/2023
![Python for Everybody on Coursera | University of Michigan]()
I developed a strong understanding of Python fundamentals, web data access, and database interaction using Python.
- 🇬🇧 English
- 🇲🇦 Arabic
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