Computer Vision Engineer assigned to your roadmap.
A Computer Vision Engineer builds systems that work from images and video. Detection, classification, segmentation, and running them fast enough to be useful.
Assigned under one service agreement with a minimum monthly capacity in hours, run by a service delivery manager in Chiang Mai, Thailand — five hours ahead of Northern Europe.
This role is delivered as part of our AI engineering services. Take the role on its own, or the whole discipline as one delivery team.
What a Computer Vision Engineer does on a managed AI engineering team.
Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.
Computer Vision Engineer skills and technology we assign for
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Object detection, classification and segmentation models trained on your own data.
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Image and video pipelines, including preprocessing and annotation workflow.
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Inference optimisation for edge or real-time deployment where latency is the constraint.
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Managing dataset versioning so a model can be traced back to the exact data it was trained on.
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Handling the edge cases that break vision systems in production, such as lighting, occlusion and unusual angles.
Seniority levels we assign.
Senior. Vision work has a wide gap between a working notebook and a deployable system. Seniority is one of the inputs in how the monthly fee is built.
When a Computer Vision Engineer is the right assignment.
Specialist assignment, usually where the product has a physical or document-processing component. On an ongoing roadmap this role is most often assigned alongside an AI Engineer or an LLM Engineer. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. It is one of the assignments that make up managed AI services at Azendo.
Questions about assigning a Computer Vision Engineer.
Do we need labelled training data?
Usually some. We can advise on annotation workflow and, where a pre-trained model gets close enough, avoid the labelling cost entirely.
Can models run on edge devices?
Yes, with quantisation and optimisation. The constraint is usually latency budget rather than accuracy.
Add computer vision engineer capacity to your roadmap.
Tell us the scope and the stack. We come back with the profile, the capacity and what the first month looks like, or you can talk to a service delivery manager first.
Assign a Computer Vision Engineer to your roadmap.
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