class MLEngineer:
def __init__(self):
self.name = "Au Amores"
self.role = "AI/ML Engineer | ML Security Specialist"
self.location = "GMT+8 | Open to Remote/US Hours"
self.stack = ["PyTorch", "TensorFlow", "FastAPI", "Docker", "AWS"]
self.focus = ["Computer Vision", "Adversarial ML", "MLOps for Security"]
self.building = "Real-time threat detection with CNNs"
self.ask_me_about = ["Model deployment", "CV pipelines", "MLsec"]
def contact(self):
return "Let's ship secure AI systems together"
me = MLEngineer()
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Android mobile app for AI-powered facial analysis Real-time alcohol intoxication detection from facial images using CNN, SVM, and hybrid models. Complete on-device computer vision pipeline with 98.4% accuracy through advanced feature extraction and model comparison. Tech Stack: |
ML-powered system for detecting phishing & malicious URLs URL threat classification system using Random Forest, XGBoost, and SVM to detect and categorize malicious web links. Achieved 96.2% accuracy with <100ms latency. Tech Stack: |
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Real-Time Phishing Detection System Advanced security app leveraging NLP to detect and classify phishing messages in real-time. Built with Android frontend and Python Flask backend for instant risk assessment. Achieved 98.4% accuracy in identifying malicious communications. Tech Stack: |
NLP-powered spam classifier with real-time text analysis Machine learning based spam classifier using NLP techniques including TF-IDF vectorization and Naive Bayes classification. Achieved 97.1% accuracy on SMS spam dataset. Tech Stack: |
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Token-based authentication backend built with Flask and JWT Secure user registration, password hashing with bcrypt, login validation, and protected API routes using Bearer tokens. Complete implementation of modern authentication best practices. Tech Stack: |
ML-powered security tool that classifies SQL queries Security focused project demonstrating SQL injection vulnerabilities and detection. Features a Flask API backend with a real-time web interface for testing injection payloads. Achieved 95.8% detection rate. Tech Stack: |
Comprehensive beginner-to-advanced cybersecurity guide
A structured learning path featuring:
- Curated resources and tools
- Hands-on labs and projects
- Clear progression from basics to advanced topics
- Industry-relevant skills and certifications
