Summary
Christophe Karam is a Deep Learning and Full Stack Engineer with 8 years of experience building production-ready ML systems and web platforms, currently leading product and technical work on a SaaS for post-disaster damage assessment. He blends hands-on deep learning (segmentation, object detection across medical, geospatial, infrared domains) with full-stack and DevOps/MLOps expertise to ship reliable, long-running workloads and high-availability inference pipelines. His background spans academic research and industry internships—from BMW logistics robotics to medical imaging and agricultural pest detection—giving him a strong foundation in both signal/image processing and applied NLP. Comfortable across FastAPI, Node/React, containerized cloud deployments and GPU clusters, he routinely designs infrastructure for large-scale data collection, training and deployment. An early robotics club leader who builds tooling as well as models, he brings a systems-minded approach that favors observability, automation and pragmatic speed-accuracy trade-offs.
7 years of coding experience
6 years of employment as a software developer
French Baccalaureate Sciences - Mathematics, French Baccalaureate Sciences - Mathematics at College Notre Dame de Jamhour
Master of Science - MS Signal and Image Processing, Master of Science - MS Signal and Image Processing at Grenoble INP - Phelma
Bachelor of Engineering - BE Mechanical Engineering, Bachelor of Engineering - BE Mechanical Engineering at American University of Beirut
French, English, Arabic