Curriculum Vitae
Curriculum Vitae of Arthur Cartel Foahom Gouabou - Lead AI Engineer, Data Scientist
Professional Experience
Lead AI Engineer
Design and development of AI algorithms for dermatological diagnostic assistance and medical workflow optimization in DermaScan centers.
Key Missions
- Leading the full cycle: architecture, engineering, deployment and technical reviews.
- Recruiting and coordinating external developers; translating business needs into delivery.
- Developing a triage model combining images and clinical metadata, deployed in 6 centres.
- OCR + LLM report-structuring pipelines and LLM agents for operational tasks.
- Internal full-stack tools, APIs, SSO and AWS, Vercel and Docker infrastructure.
- Collaboration with dermatologists and LIS, data governance and team support.
Tech Stack
Senior Data Scientist
Leading the development of AI solutions for container flow management optimization for key logistics players (shipping companies, terminals, freight forwarders).
Key Contributions
- Developed a GenAI-powered insights engine using Retrieval-Augmented Generation (RAG) to enable clients to query and analyze CO₂ KPIs through natural language.
- Built a document intelligence pipeline combining OCR and LLMs to automate data extraction from scanned documents.
- Designed and deployed time series forecasting modules to predict traffic, capacity needs, and terminal appointments.
- Engineered scalable APIs and microservices using FastAPI, Docker, Kubernetes, and Airflow.
- Conducted exploratory data analysis on large heterogeneous logistics datasets.
- Developed machine learning models for prediction, classification, and optimization.
- Designed data pipelines and integrated multiple internal and external sources.
- Cloud deployment and MLOps using Scaleway Cloud, Prometheus, and CI/CD workflows.
Tech Stack
Temporary Lecturer and Researcher (ATER)
Conducted applied research in deep learning for 2D/3D medical imaging. Taught and mentored students in advanced computing and engineering programs. Published in peer-reviewed venues.
Domains
Researcher in Computer Vision
Developed deep learning models for skin cancer detection. Focused on dataset design, CNN architectures, and cross-institutional research collaborations in medical AI.
Research Intern (AI & CV)
Designed a proof-of-concept diagnostic system for melanoma using early deep learning models (SVM, CNN, MLP). Focused on preprocessing, feature selection, and evaluation.
Core Skills
Deep Learning & ML
Advanced proficiency in PyTorch, TensorFlow, Scikit-learn, and model deployment. Experience includes LLMs, RAG pipelines, CNNs, and time series forecasting.
Computer Vision
Specialized in medical image analysis, segmentation, classification, and object detection using deep learning frameworks.
Time Series
Development of end-to-end pipelines for KPI prediction, anomaly detection, and real-time time series forecasting in production environments.
MLOps & DevOps
Experienced in containerization (Docker), orchestration (Kubernetes), and CI/CD workflows. Deployment via FastAPI, Airflow, and cloud platforms like Scaleway and AWS SageMaker.
Databases
Expertise in SQL and NoSQL (MongoDB). Design and optimization of relational schemas and analytical queries for ML pipelines.
Python
Python power-user with deep experience in NumPy, Pandas, Matplotlib, PyTorch, TensorFlow, Keras, and API development using FastAPI and Django.
Languages
French: native
English: professional B2
Ghomala’
Education
PhD in Computer Science · computer vision
Research Focus: Deep Learning, Medical Image Analysis, Skin Cancer Detection, Explainable AI.
Master’s degree in vehicle engineering and mechatronics · top of class
ENSPD – National Polytechnic School of Douala
Engineering degree in mechatronics