Looking for a job

audio spectrum

@audiospectrum

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I build AI-driven software for cybersecurity, computer vision, and LLM applications.

Tunisia

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What I'm looking for

I'm looking to contribute to innovative software and AI solutions, especially work involving AI-driven applications, machine learning, cybersecurity, computer vision, and LLM-based systems.

I've built AI-driven applications spanning Android cybersecurity, computer vision, and LLM-based software. At TELNET, I developed ATT&CKDroid, an explainable Android malware detection application that maps permissions and sensitive APIs to MITRE ATT&CK Mobile techniques.

I trained an ensemble of Random Forest, Extra Trees, XGBoost, and LightGBM models to reach 91.37% accuracy, with GPT-4o-mini escalation increasing accuracy to 92%. At ENIS–Moncton, I evaluated Android malware models against adversarial attacks on Compute Canada infrastructure and developed defenses that reduced attack success by about 50%.

I've also built a YOLO, Flask, and Android bakery detection system, a PyQt5 tool for automated DLT filtering, and RAG pipelines for LLM test-oracle generation and arXiv paper recommendations.

Experience

Work history, roles, and key accomplishments

PFE Intern

TELNET

Feb 2026 - Jun 2026 (4 months)

Developed ATT&CKDroid, an AI-powered Android malware detection app using a GraphRAG-based pipeline. Trained an ensemble model achieving 91.37% accuracy, escalating complex cases to GPT-4o-mini for deep analysis and pushing accuracy to 92%.

Graph RAG Android Machine Learning Random Forest Extra Trees XGBoost LightGBM GPT 4o Mini Cybersecurity

Python Development Intern

KPIT

Jul 2025 - Aug 2025 (1 month)

Designed a PyQt5 desktop application that converts DLT Viewer XML filters into executable Python scripts for automated DLT filtering. Architected the application using OOP and MVC to ensure long-term scalability and maintainability.

Python PyQt Object Oriented Programming MVC DLT

Adversarial Machine Learning Research

ENIS – Moncton, Canada

Nov 2024 - Apr 2025 (5 months)

Reproduced state-of-the-art Android malware detection models and ran large-scale experiments on Compute Canada HPC infrastructure. Applied adversarial attack techniques and designed defense strategies, reducing attack success rate by ~50%.

Adversarial Machine Learning Android Malware Detection HPC APK Analysis

Computer Vision Intern

CRNS

Jun 2024 - Jul 2024 (1 month)

Built a real-time bakery product detection and counting system using YOLO, Flask, and an Android application. Enhanced recognition performance by integrating CLIP into the detection pipeline, achieving 80% accuracy and a 15% improvement in recognition performance.

Computer Vision YOLO Flask Android CLIP