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│ 🌍 I solve problems others choose to forget |
│ │
│ While others optimize for GPU clusters, │
│ I optimize for reality: │
│ • $50 Android phones from 2015 │
│ • 2G internet, 512MB RAM │
│ • Pay-per-kilobyte data │
│ • 1.3 billion people at the edge │
└─────────────────────────────────────────────────────┘
Production-ready ML models optimized for African infrastructure
[Explore Epital Elderguard] →](https://github.com/petitmj/-Epital-ElderGuard---AI-Powered-Fall-Detection)
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║ 40+ ML models deployed on Edge Devices ║
║ 2.4M+ end users (95% on <2GB RAM devices) ║
║ $4K+ saved in cloud costs ║
║ <100ms average inference latency ║
║ 80% of models work completely offline ║
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Edge ML & Optimization:
TensorFlow Lite ONNX Runtime PyTorch Mobile Quantization Pruning Distillation Federated Learning ARM Optimization
Languages:
Python JavaScript/TypeScript C++ Rust PHP
Frameworks:
TensorFlow PyTorch scikit-learn Django React Next.js WordPress
Infrastructure:
Linux/Ubuntu Docker Kubernetes Edge Computing IoT CI/CD
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Remote roles where I can:
- ✅ Build ML systems for global users (not just SF)
- ✅ Optimize for constraints (better engineering)
- ✅ Write docs people actually understand
- ✅ Contribute to meaningful open source
Target: Companies serious about "democratize [X]" globally
Automattic • Zapier • Canonical
"The constraints of building for Africa make me a better engineer for everyone. I have learnt to respect bandwidth, memory, and compute. I ship lean, efficient, elegant solutions."