MOHAMED AKRAM LAMHOUR

Lead engineer for a construction management platform handling workflows, surveys, reports, scheduling, and ops data. Built a scalable Django/DRF API with JWT, RBAC, Celery, and optimized queries. Developed a React/TS frontend with FullCalendar, complex forms, and i18n. Set up CI/CD with GitHub Actions and Docker for zero-downtime deploys. Stack: Django, DRF, React, TS, PostgreSQL, Redis, Celery, Docker.

Developed YOLOv8 models for underwater fish detection and tracking using sonar and video data. Achieved 85% detection accuracy in low-visibility underwater environments. Built annotation and training pipelines using CVAT and custom preprocessing scripts. Researched techniques for computing similarity between annotations.

Configured HR information systems based on business requirements. Developed SQL workflows to maintain data quality and consistency. Produced HR dashboards, reports, and end-user documentation.

• Managed internal communications through email and memos to deliver announcements. • Gathered and analyzed feedback from customers to gauge satisfaction. • Addressed customer complaints and resolved issues. • Maintained a tidy workplace to support cleanliness and quality standards. • Collaborated with finance teams regarding targeted budgets. • Scouted and booked venues for events and conferences. • Formulated and submitted progress reports to management. • Trained and assigned tasks to team members.

Developed an IoT-driven intelligent irrigation system using cloud computing and LSTM/GRU models. Increased crop productivity by 30% and reduced water usage by 20%. Integrated predictive models with Raspberry Pi for real-time automated irrigation.

Autonomous Underwater Vehicles (AUV) Challenge Project Title: Detection and tracking of fish through sonar imagery and underwater video analysis using Deep Learning. • Tools: CVAT, YoloV8, Deep Learning.