Medindev, backend and AI engineer
MSc graduate • Open to remote work

I build reliable APIs and intelligent systems that ship.

Java Spring, FastAPI, PyTorch and DevOps for production teams.

Full-stack engineer and AI researcher turning complex backend, MLOps and traffic-intelligence problems into fast, maintainable products.

MSc Computer Science graduatePublished AI researchOpen to remote jobs & projects
5+
years building
+24.3%
AI thesis gain
<100ms
API inference

My Technical Skills

Backend, AI/ML, DevOps, Frontend

4+ Years Production Expertise

Backend Engineering

Java (Spring Boot 3)Python (FastAPI / Async)C++20 (High Perf.)Microservices & KafkaPostgreSQL / Redis
Impact
API Latency -40%

Artificial Intelligence

Deep Learning (PyTorch)Vision (YOLOv8 / OpenCV)Graph Networks (ST-GCN)MLOps (MLflow / Docker)Inference (TensorRT)
Impact
+24.3% Accuracy (Thesis)

DevOps & Infrastructure

Docker / Kubernetes / HelmCI/CD (GitHub Actions)Cloud (AWS / AliCloud)Monitoring (Prometheus)Linux & Scripting
Impact
Deploy Time < 5min

Modern Frontend

Next.js 15 (App Router)TypeScript / React 19Tailwind v4 / GlassmorphismFramer MotionMobile-First Design
Impact
Pixel-Perfect UX

My Technical Projects

Backend, AI/ML, DevOps in Production

Architecture, Code, Concrete Results

Secure Management API
Python

Secure Management API

PostgreSQLJWT
Microservices + Kafka
Spring Cloud

Microservices + Kafka

KafkaRedis
High-Perf HTTP Server
C++20

High-Perf HTTP Server

Boost.AsioMulti-thread
GitHub Actions

Complete CI/CD Pipeline

DockerK8s
Python

End-to-End MLOps Pipeline

Scikit-learnMLflow
PyTorch

Computer Vision (AI)

YOLOv8CUDA

AI Research & Master's Thesis

Unified Traffic Perception & Prediction

YOLOv8 + ST-GCN • Weather-Adaptive Fusion

Unified Traffic Perception & Prediction for Adverse Weather Conditions

YOLOv8ST-GCNPyTorchFusionReal-Time

Intelligent Transportation Systems (ITS) rely heavily on deep learning, but existing models suffer from brittleness in adverse weather (fog, night) and siloed operation. I developed a novel, real-time framework integrating YOLOv8 object detection with ST-GCN spatiotemporal forecasting. The core innovation is a bidirectional, weather-adaptive fusion engine that dynamically balances models based on environmental conditions, achieving a 24.3% performance improvement.

Detection Acc
78.4%
Improvement
+24.3%
Latency
67.2ms
FPS
14.9
model.py
class WeatherAdaptiveFusion(nn.Module):
  def __init__(self):
    super().__init__()
    self.alpha_det = nn.Parameter(torch.tensor(0.5))
    self.alpha_pred = nn.Parameter(torch.tensor(0.5))

  def forward(self, det_features, pred_features, weather_context):
    # Dynamic weighting based on weather conditions
    w_det, w_pred = self.get_adaptive_weights(weather_context)
    
    # Bidirectional feature fusion
    fused_state = w_det * det_features + w_pred * pred_features
    return fused_state

Performance Under Adverse Weather

Fusion Architecture

Input (Video)
YOLOv8
ST-GCN
Adaptive Fusion
Output

ST-GCN Network

Sensors
Spatial
Temporal
Risk
Real-Time Inference
input: Frame #204 (Rainy)
detect: [Car: 0.92, Bus: 0.88]
pred: "High Collision Risk"

Scientific Publications

IEEE Conferences, Impact Factor Journals

ST-GCN, YOLO, Traffic Analysis

A Multi Stage Ensemble Framework for Fake News Detection: Integrating Traditional Machine Learning, Deep Learning, and Advanced Feature Engineering

International Journal of Scientific Research and Management, Vol. 14 Issue 03 (Published)2026DOI: 10.18535/ijsrm/v14i03.ec05

Published on Mar 27, 2026, pages 2802-2817. This journal article presents a four-stage ensemble framework for fake news detection that combines traditional ML, deep learning, and advanced feature engineering for robust binary classification.

Enhanced Deep Learning Models for Real-Time Traffic Analysis

IEEE Access (Under Review)2026IF 3.9

Comprehensive study on joint optimization of ST-GCN and YOLO for real-time traffic analysis under adverse weather conditions.

Evaluation of Deep Learning Architectures for Traffic Sign Classification

J. Traffic & Transport Eng. (Under Review)2025IF 2.5

Quantified 18.5% generalization gap across 10 architectures (ResNet, ViT, EfficientNet) and 4 datasets. Benchmarked adversarial robustness (PGD/FGSM) and hardware efficiency (Edge vs Server).

Leak-Free ML for Real-Time Urban Crash Severity Prediction

Intl. Conf. on Smart Cities (Under Review)2025Conf

Engineered a leak-free pipeline with 124 features from 166k NYC records. Achieved 0.960 Macro-F1 and <0.1ms latency using cyclical temporal encoding and spatial clustering.

4 Publications
1 Published • 3 Under Review
Applied Research
About

About Medindev

Backend Engineer & AI Researcher

Medy Evrard MISSANG MI ABAA

Medy Evrard MISSANG MI ABAA

China (Nanjing) | Remote

Languages

🇫🇷French
Native
🇬🇧English
Fluent
🇨🇳Mandarin
Basic

Code that bridges continents & disciplines

Results-driven backend engineer with expertise in Java/Spring Boot, Python FastAPI, Node.js/Express, C/C++, and cloud-native deployment. MSc Computer Science graduate (Nanjing) with 4+ years building scalable APIs. Open to remote jobs and new projects.

Born and raised in Gabon, I left my home country in 2018 to explore the world. Educated across cultures with coursework in South Africa and China, I bring a multilingual mindset to engineering: French precision, English pragmatism, and a dash of Mandarin patience.

My Journey

2018Left Gabon → Start of International Journey
2020Started BS Computer Science & Founded MedinChina
2024Graduated BS CS & SEO Specialist at Quick Dogthis
2025API Specialist at APIDog
2026Graduated with an MSc in Computer Science

Core Interests

Ethical AI
Optimization
Africa Tech

Curriculum Vitae

Experience, Education, Certifications

Direct PDF Download

Downloaded 847x

A4 Format • 3 Pages • English/French

Experience

API Developer & Tech Specialist

APIDog2025-Present

Content & SEO Specialist

QUICK DOGTHIS2024-2025

Founder & Tech Lead

MedinChina Sourcing2020-Present

Education

Master's in Computer Science

Nanjing University of IST 2024-2026

B.S. Computer Science

Sanming University 2020-2024

Certifications

AWS Certified DevOpsDocker Certified

CV updated March 2026

Contact for Opportunities

Open to Remote Jobs & Projects

Backend • AI/ML • DevOps • Custom Projects

Available for new opportunities • Response within 24h

Direct Contact

Email
medtexprog@gmail.com
WhatsApp Business
+86 195 7530 6452
Direct chat
Availability
Mon-Fri 9am-8pm GMT+1 Weekend urgent projects
Zone
Africa → World Remote Global
© 2026 Medindev - Medy Evrard MISSANG MI ABA'A