
Hana Ahmed
@hanaahmed2
AI Engineer building computer vision systems, including a space-debris detector that won 1st place in EGSA.
What I'm looking for
I developed an onboard camera system for space-debris detection using YOLO26n, with an Unscented Kalman Filter for state estimation and trajectory tracking. The graduation project won 1st place in the EGSA competition, 4th place in the Made in Egypt competition, and reached the finals of SwiftAct.
At Instant | Advanced AI, I trained in deep learning and implemented computer vision and multimodal AI workflows. I also built a LangChain and ChromaDB RAG system for question-answering over PDFs, and a Streamlit platform bringing together YOLO models for six use cases.
Experience
Work history, roles, and key accomplishments
AI Trainee
National Telecommunication Institute
Oct 2026 - Jan 2027 (3 months)
Building a foundation in machine learning, deep learning, data preprocessing, and neural network architecture. Studying computer vision, LLMs, and MLOps.
AI Intern
Instant
Jul 2025 - Aug 2026 (1 year 1 month)
Trained in Deep Learning, Transformers, Vision Transformers, GANs, and NLP through hands-on project work. Implemented AI workflows including object detection, image segmentation, and multimodal AI pipelines.
PR Director
IEEE Student Branch, Helwan University
Sep 2024 - Aug 2026 (1 year 11 months)
Organized technical and non-technical events, coordinating logistics for student participation. Led communication and promotional strategy to raise visibility of branch activities and initiatives.
Embedded ARM Development Intern
Information Technology Institute
Aug 2025 - Sep 2025 (1 month)
Designed and programmed ARM-based microcontroller applications for embedded use cases. Developed integrated hardware-software systems, bridging firmware logic with physical peripherals.
Education
Degrees, certifications, and relevant coursework
Helwan University
Bachelor's Degree, Communication and Electronics Engineering
2021 - 2026
Grade: 77.85% (Very Good)
Pursuing a Bachelor's degree in Communication and Electronics Engineering with an overall grade of 77.85% (Very Good).
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Salary expectations
Job categories
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