Raed
Al Tabanjeh
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.
Three Domains of Practice
Each domain informs the others. The intersections are where the most interesting systems emerge.
Machine learning pipelines, OCR systems, NLP applications, data platforms, and intelligent automation — built for production, not prototypes.
Edge computing, industrial cameras, sensor networks, SCADA integration, and smart monitoring — bridging physical processes with digital intelligence.
Applied research methodologies, systematic thinking frameworks, and knowledge architectures — converting ideas into structured, testable assets.
Systems & Projects
Most clinics operate with disconnected tools across front desk, clinical care, diagnostics, pharmacy, billing, HR, and operations. This causes slow handoffs, duplicated data entry, weak …
Pharmaceutical packaging lines require verification of printed batch numbers and expiration dates to ensure human readability and regulatory compliance. Manual inspection is slow, error-prone, and …
Hydroponic farming requires precise, continuous management of nutrient solution composition, pH, electrical conductivity (EC), dissolved oxygen, and environmental parameters. Manual monitoring introduces lag, inconsistency, and …
Enterprise HR teams process thousands of CVs manually per hiring cycle. This is slow, inconsistent, and subject to unconscious bias. Structured evaluation criteria are rarely …
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
Active Thinking
Using distributed sensor networks, flow meters, pressure transducers, and AI to detect water leaks, monitor infrastructure health, and …
Multi-variable ML model integrating soil sensors, weather APIs, satellite NDVI imagery, and historical yield data to forecast crop …
Real-time vibration analysis and acoustic anomaly detection for industrial machinery — predicting bearing failures, misalignments, and wear patterns …
Engineering Thought
The gap between a working model and a deployed system is where most value is lost.
Maintainability is not a feature — it is the foundation of every serious system.
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.