Mohammad Jahangir — Riyadh, Saudi Arabia
AI & ML Development
AI/ML work here is applied, not academic: it's integrated into existing product flows to remove a specific manual step. In Opperiq, that's face/selfie verification (YOLOv8, InsightFace) for secure attendance and OCR for receipt/expense processing. On the MMR data-center program, it's AI-assisted document classification and routing between Aconex and SharePoint, and structured extraction of review comments from technical submittals.
What this looks like in practice
- Building an AI-powered face/selfie verification service (YOLOv8, InsightFace) with model caching for low-latency mobile verification
- Building OCR-based receipt processing that extracts expense data directly into a finance workflow
- Building AI-assisted document classification and routing between Aconex and SharePoint for a construction program
- Training and deploying ML models with end-to-end pipelines (preprocessing, feature engineering, model serving via Flask and FastAPI)
Frequently asked
Does Mohammad Jahangir build AI/ML features or just call third-party APIs?
Both, depending on the problem — Opperiq's face verification uses YOLOv8 and InsightFace directly with model caching for performance, while other workflows use trained models served through Flask/FastAPI pipelines.
What's a concrete AI feature currently in production?
Opperiq's face/selfie verification for employee attendance, and OCR-based receipt processing that feeds directly into the platform's finance module.
Technologies
Related work
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