Systems Architect & AI Engineer — Jordan

Raed
Al Tabanjeh

Systems Architect | Industrial Intelligence Builder

I design and deploy intelligent systems at the intersection of AI, Industrial Automation, and Data Engineering. My work converts real-world problems into operational, maintainable systems — not demos.

View Systems → About Me Ideas Lab ↗
12+
Systems Built
5
Domains
∞
Ideas Lab
What I Build

Three Domains of Practice

Each domain informs the others. The intersections are where the most interesting systems emerge.

⬡
AI & Data Systems

Machine learning pipelines, OCR systems, NLP applications, data platforms, and intelligent automation — built for production, not prototypes.

Python PyTorch ONNX FastAPI PostgreSQL
◈
Industrial & IoT Systems

Edge computing, industrial cameras, sensor networks, SCADA integration, and smart monitoring — bridging physical processes with digital intelligence.

IoT MQTT Edge ML PLCs OPC-UA
◇
Knowledge & Thinking Systems

Applied research methodologies, systematic thinking frameworks, and knowledge architectures — converting ideas into structured, testable assets.

Systems Thinking Research Django APIs
Selected Work

Systems & Projects

All Projects →
⬡ AI & Data
Industrial OCR Inspection System

AI-powered vision system verifying printed batch and expiration data on pharmaceutical packaging using computer vision at line speed.

Python ONNX PaddleOCR Edge
◈ Industrial / IoT
Hydroponic AI Control System

Closed-loop AI control system for hydroponic farms — managing nutrients, pH, EC, and environmental parameters using sensor fusion and ML models.

IoT MQTT ML Django
⬡ AI & Data
AI Applicant Tracking System

End-to-end ATS with AI-powered CV screening, semantic ranking, and structured candidate evaluation pipeline built for enterprise HR workflows.

NLP FastAPI PostgreSQL React
◈ Industrial / IoT
IoT Farm Monitoring Platform

Distributed sensor network for agricultural monitoring — real-time data ingestion, anomaly detection, and decision support for field operators.

IoT InfluxDB Grafana Python
Engineering Philosophy

How I think
about systems

Every system I build starts from a question: what problem does this actually solve? Then architecture, then code.

  • › Real-world deployment over lab demos
  • › Data-driven decisions over assumptions
  • › Systems thinking across the full stack
  • › Maintainability as a first-class constraint
  • › Science-backed methodology always
Read my approach →
system_design.py
# System design starts with the right questions def design_system(problem): requirements = extract_real_constraints(problem) architecture = select_minimal_viable(requirements) # Never skip validation assert architecture.is_testable() assert architecture.is_deployable() assert architecture.solves(problem) return architecture.build() # Knowledge graph: Idea → Research → Build → Deploy pipeline = ["observe", "model", "test", "deploy"]
Ideas Lab

Active Thinking

Researching
National Water Intelligence Platform
Researching
Predictive Maintenance ML Pipeline
Researching
Agricultural Yield Forecasting System
All ideas →
Writing

Engineering Thought

01
Systems Design8 min read
Why Most AI Projects Fail Before They Start

The gap between a working model and a deployed system is where most value is lost.

02
Engineering Thinking6 min read
Building Systems That Outlive Their Creators

Maintainability is not a feature — it is the foundation of every serious system.

All writing →
Work Together

Have a hard problem
that needs a system?

I work on industrial AI, data platforms, IoT infrastructure, and knowledge systems. If it is a real problem in the real world — let's talk.

Start a conversation →