Science, Technology, Engineering & Mathematics (STEM)
Computer Vision Engineer
Image and video analysis AI
What this life looks like
You teach computers to see. Computer Vision Engineers build AI systems that can interpret images and video — enabling machines to recognise faces, detect objects, read text, analyse medical scans, and monitor agricultural fields from satellite imagery. It is one of the most visually impactful branches of artificial intelligence.
What she actually does
You design and train machine learning models that process visual data, build image classification and object detection systems, develop video analysis pipelines, implement computer vision algorithms in production applications, work on medical imaging analysis, satellite image processing, and quality control in manufacturing — anywhere visual inspection matters.
Where you can work
| Sector | Places in Africa |
|---|---|
| AgriTech Startups | Zenvus (Nigeria), Aerobotics (South Africa), Apollo Agriculture (Kenya) — drone and satellite vision |
| Health Tech | Ubenwa (cry analysis AI), Deepcare — applying vision AI to African healthcare diagnostics |
| Mining & Resources | Computer vision for ore sorting and safety monitoring in African mines |
| Research Institutes | Lacuna Fund, Deep Learning Indaba affiliates, AIMS research groups |
Why it matters in Africa
Computer vision is being applied to uniquely African problems: detecting crop diseases from phone photos, counting livestock from drone imagery, identifying malaria parasites in microscope images, and reading hand-written forms. African computer vision engineers building solutions for African contexts are doing uniquely impactful work.
Your pathway in
- Build strong programming and mathematics foundations, particularly linear algebra
- Study Computer Science, Mathematics, or Electrical Engineering at university
- Learn Python and computer vision libraries — OpenCV, TensorFlow, and PyTorch are the core tools
- Study the theory and practice of convolutional neural networks (CNNs) — the backbone of computer vision
- Build a portfolio of computer vision projects and publish them on GitHub
Skills you will need
- Python and computer vision libraries (OpenCV, PyTorch)
- Deep learning and CNN architectures
- Image processing fundamentals
- Dataset creation and annotation
- Model deployment
- Mathematical foundations (linear algebra, calculus)
A woman in this field
Nneile Nkholise
South Africa
Nneile is a mechanical engineer and founder of iMed Tech Group, a South African biotech company using Computer-Aided Design (CAD) and 3D printing to create custom medical solutions including breast prostheses. She later co-founded 3DIMO for livestock management technology. She was named in Forbes 30 Under 30 for 2018 and received the South African Youth Award for Science, Innovation, and Technology.
Take your first step
- Install OpenCV in Python and build a simple image filter or face detector
- Complete fast.ai's free Practical Deep Learning course at fast.ai — it covers computer vision thoroughly
- Look into the Lacuna Fund at lacunafund.org, which supports African computer vision dataset creation
- Join the Deep Learning Indaba community at deeplearningindaba.com