I build governed AI. Not prompts. Architectures. Systems that are not allowed to fail.

_{Principal Engineer · Agentic AI · Core Banking (Temenos T24) · OWL2 · MCP · RAG/KAG · World Bank & Central-Bank Programs}

As a Principal Engineer working across core banking (Temenos T24) and World Bank & central-bank programs, I sit where data science meets enterprise architecture. I don't just train models — I architect the governed systems that put them into production where failure is not an option. Years shipping fiscal & banking infrastructure — a fiscal POS certified by the Ministry of Finance across 5,000+ stations, and an ODS certified on Temenos Exchange — taught me what "production-grade" actually costs. I bring that same discipline to AI: observability, idempotency, durability, and governance, applied to LLMs and semantic reasoning.

My edge: most people can train a model or ship a system. I do both — and I make the model behave inside the system.

I build agentic, knowledge-grounded AI that operates inside a governed semantic world — not a chatbot, an architecture:

flowchart LR
  D["Domain Data<br/>SQL · Documents · Streams"] --> S
  subgraph GOV["🔒 Governed Semantic Layer"]
    direction TB
    S["OWL Ontologies<br/>SWRL Rules · RDF4J"] --> K["KAG Retrieval<br/>SPARQL · Vector · BM25"]
  end
  K --> A
  subgraph AGENT["🤖 Agentic Orchestration"]
    direction TB
    A["LLM Reasoner<br/>think · act · observe"] <--> T["Tools · MCP<br/>code · search · gen"]
    A --> V["Verify · Guardrails<br/>adversarial checks"]
  end
  V --> O["⚙️ Governed Action<br/>sign · persist · serve"]
  O --> M["📈 Observability<br/>cost · durability · audit"]
  M -. feedback .-> A

A reactive Operational Data Store for Temenos T24 core banking — certified and published on Temenos Exchange (the marketplace of 3,000+ member banks) and adopted by 3+ banks (ATB, Baraka Bank, BH). @ UniQ Soft Technology.

What it is. A signals-based, low-latency ODS that models the data-warehouse architecture (COB export via T24 DW.Export) and drives a compliance-grade account-reconciliation workflow engine with a full audit trail.

More than a data store — what I built around it:

• Semantic AI layer — OWL ontologies (PMBOK · ISO 31000 / 27000 · IFRS · FRM) + SWRL inference + RDF4J/SPARQL agents reasoning over live banking data.
• Offshore Risk KPIs for Warba Bank (Kuwait) — Market / Liquidity / Cost risk via SSIS ETL from T24 FRM → SSRS, with zero COB breach.
• End-to-end MLOps (CRISP-DM) — predictive & classification models on internal banking data, served via PMML.

Why it matters. Core-banking data is unforgiving — a COB breach is a regulatory event, not a bug. This shipped certified on Temenos Exchange, with zero COB breach.

Temenos T24 · Java / JEE · Django · RDF4J · OWL · SWRL · SPARQL · SSIS / SSRS · Oracle PL/SQL · Kafka · Redis · gRPC · Spark / Scala  |  🔒 Described faithfully from the record.

Hands-on, end-to-end — from raw signal to served decision:

• Computer Vision / OCR — Arabic document OCR pipelines (deskew sweeps, glare/label removal, multi-engine fallback) on real Tunisian ID & fiscal documents.
• NLP / NLU — NER + fuzzy entity resolution, semantic invoice checkers, intent classification across EN / FR / Tunisian.
• Classical ML — risk scoring (credit default, tax-risk), feature selection (RFE/RFECV), dimensionality reduction, cross-validated model selection.
• Semantic AI — ontology-driven fraud detection with SPARQL + SHACL over knowledge graphs.

_{📊 Deep-dive ML / DS portfolio → @MuhamedHabib}

No placeholder projects — everything below runs in production.

AI · ML · Data

Agentic & Semantic AI

Enterprise Backend & Platform

Cloud & DevOps

🏆 Pair Extraordinaire ×3 · Pull Shark ×3 · YOLO · Quickdraw  |  🌍 GSoC 2026 — Accord Project (Linux Foundation): agentic workflow + LLM template-logic executor.