struct Engineer {
name: &'static str,
role: &'static str,
company: &'static str,
education: &'static str,
location: &'static str,
specialization: Vec<&'static str>,
achievements: Vec<&'static str>,
}
impl Engineer {
fn new() -> Self {
Self {
name: "Rayane Aboud",
role: "Software & Systems Engineer",
company: "Ouedkniss - Algeria's Leading Classifieds Platform",
education: "ESI Algiers - State Engineer & Master 2 (GPA: 3.7/4.0)",
location: "Algiers, Algeria",
specialization: vec![
"High-Performance Systems",
"Edge AI & MLOps",
"Distributed Systems",
"Performance Optimization",
"Cloud-Native Technologies"
],
achievements: vec![
"Migrated core services to Rust - reduced latency & improved throughput",
"Consolidated 40 microservices to 8 pods - maintained 99.9% availability",
"Built LSTM pipeline with sub-100ms inference on edge devices",
"Published research at MedCCAI 2025"
],
}
}
}
|
Rust |
C/C++ |
Python |
Go |
JavaScript |
TypeScript |
|
PyTorch |
TensorFlow |
Computer Vision |
Scikit-learn |
|
PostgreSQL |
MySQL |
MongoDB |
Redis |
Kafka |
|
Docker |
Kubernetes |
Linux |
Git |
Grafana |
|
FastAPI |
React |
Tauri |
NestJS |
- β‘ Performance Optimization: Migrated core services from PHP to Rust, dramatically reducing system latency and improving throughput for millions of daily active users
- ποΈ Infrastructure Consolidation: Reduced microservice footprint by 80% (40β8 pods) through strategic profiling and optimization, maintaining 99.9% availability while cutting cloud costs
- π Caching & Prediction: Designed asynchronous caching algorithms and predictive models to enhance user experience and system responsiveness
- π οΈ Developer Tooling: Built a Service Supervision Tool using Rust + Tauri β a desktop application for simplified service management with real-time log visualization and environment configuration management
- π Real-Time ML Pipeline: Developed LSTM-based time-series forecasting for energy prediction with sub-100ms inference latency on edge devices
- π Secure Federated Learning: Engineered encrypted MQTT/TLS protocol for privacy-preserving federated learning in smart building environments
- π Production Deployment: Optimized inference pipelines integrated with Grafana dashboards for real-time monitoring
- π Research Publication: Contributed to research published at MedCCAI 2025
- π Configured enterprise routing protocols (OSPF, BGP) and VLANs for large-scale network infrastructure
Tech Stack: Python | TensorFlow | FastAPI | React | Docker | YOLO
- Built end-to-end SaaS platform processing agricultural drone imagery for real-time crop health analytics
- Deployed custom YOLO models for disease detection, pest identification, and nutrient deficiency analysis
- Orchestrated scalable backend with FastAPI and containerized deployment
State Engineer & Master 2 in Computer Science | 2019 - 2024
GPA: 16.27/20 (~3.7/4.0)
Thesis: Edge AI system for optimizing energy consumption in smart buildings
Relevant Coursework: Distributed Systems | Machine Learning | Computer Networks | Software Engineering
π Deep Learning Specialization - Andrew Ng
π IBM Data Engineering Basics - IBM
|
|
|
|
| π‘ Area | π¬ Research | π οΈ Building |
|---|---|---|
| Systems Performance | Low-level optimization & profiling | High-throughput services in Rust |
| Edge AI | Model compression & quantization | Real-time inference pipelines |
| MLOps | Federated learning frameworks | Secure data aggregation systems |
| Cloud Infrastructure | Kubernetes optimization | Scalable microservices architecture |
| Language | Proficiency |
|---|---|
| π©πΏ Arabic | Native |
| π¬π§ English | Professional Proficiency |
| π«π· French | Professional Proficiency (C1) |