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Data Scientist
“In a climate this uncertain, Shakers gives you a layer of reassurance and confidence. What I like most, without a doubt, is the flexibility and getting to work on top-tier projects.”
Join ShakersShakers for expert talent
Build your career as a Computer Vision Engineer
Projects where the system that sees is the engagement: what gets detected, with which camera, and deciding on the edge or in the cloud. Shakers connects you with companies that arrive with the visual case and need the model, not a course.
Computer vision employment in Spain lives between the plant and the product: consultancy and industry on one side, medtech, sport and defence on the other. The by-project route barely had a door: global marketplaces and a seven-year-old forum.
This is not a training route: it is what gets built on a real engagement, what you must prove and where your territory ends.
- Higher-paying projects
- More continuity between projects
- Greater recognition for your experience
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AI Agent Developer
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Backend Developer & Data Engineer
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Rubén
Software Developer
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Alejandro
AI Developer
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Toño
Product Designer
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Join ShakersWhat a Computer Vision Engineer decides every week
A Computer Vision Engineer builds systems that extract meaning from images and video: object detection and segmentation with OpenCV and convolutional networks, recognition, quality inspection and deployment on the edge or in cloud. The deliverable is a machine that sees and decides.
Their boundary: the Machine Learning Engineer works general models; the Computer Vision Engineer specialises them in pixel, video and edge.
You decide what gets detected and with which accepted error margin, train with PyTorch on real images and solve the pixel with OpenCV and C++. You also decide what does not get detected: a case that fails calibration gets rejected with an argument. The camera is the business's; making the machine see is yours.
PythonOpenCVC++PyTorchTensorFlowConvolutional networksObject detectionImage segmentationVideo analyticsEdge AI2D/3D visionMATLABAzure AI VisionGoogle Cloud Vision
Object detectionOpenCVConvolutional networksVisual inspectionEdge AIVideo analytics
Free to join, no hidden fees
How Shakers works for talent
Create your profile
Tell us who you are, what you can do, the projects you want to work on and what your availability and rate are.
Get your certification
We certify your experience and your use of AI so companies understand and trust what you bring.
Match with projects
We connect you with well-paid projects that fit your expertise and your preferences.
Projects that live up to your expectations
Work on real projects, with teams that need your expertise, without wasting time looking for opportunities.
Projects that fit you
Work with companies that need your stack and experience to take their projects to production.
Opportunities that come to you
Our AI matching connects your profile with the projects that fit your preferences.
You build. Shakers handles the rest
Set your own terms based on your seniority. We manage contracts, payments and paperwork.
What a Computer Vision Engineer delivers at Shakers
The engagement arrives with the case
The piece to inspect, the camera already installed and the decision the image must unlock: a vision problem, not a tools problem.
Detection with measured precision
Model trained on the images of the case, error thresholds agreed and deployment on the edge or in cloud. Deliverables the plant understands.
The pixel and the training
OpenCV and C++ where performance rules, Python and PyTorch to train. The tool is the one the case demands, not the fashionable one.
Counted detections, not courses
Having solved real pixels and knowing how to tell it: what you detected, with which camera, with which error and what the business decided.
Measuring is also discarding
Choosing between the case the client asks for and the light that has no more to give. Sometimes redesigning the capture wins.
Where your engagement ends
You build the system that sees; general models belong to the ML Engineer and the device to the IoT. If the engagement crosses, their row joins.
Frequently asked questions
Any other questions? Write to us and we will reply.
What does a Computer Vision Engineer do by project?
They build the system that sees: detection and segmentation with OpenCV and convolutional networks, visual inspection, video analytics and edge deployment. One case, one period, one detection metric.
Can you make a living from contract computer vision?
The search reference is a seven-year-old forum. The by-project door barely existed: the engagement arrives with the visual case decided, the scope written, in the densest cluster of the AI family.
How much to charge as a contract Computer Vision Engineer?
The mechanism marks it: seniority, industrial or product sector, edge or cloud, and days against project. No local public rate reference exists; your budget closes per engagement.
Which stack does the work run on?
OpenCV and C++ for the pixel, Python and PyTorch to train, and edge or cloud vision depending on the case. OpenCV is the signature of the craft: it appears in 89 active tech ads in Spain (n=31,957).
Is there enough engagement in Spain for the role?
Employment sends the signal: 260 active tech ads in Spain mention computer vision (Shakers market analysis, n=31,957, September 2026). The by-project route was missing.
How do I get in without a contact network?
By getting certified: the process certifies your practice and the collective brings the engagements. Your work is showing real detections on real images; matching each project to your profile is ours.
The daily shape of the craft
The dataset nobody calibrated, the camera that changes light and the prototype that detects in the notebook. Less demos, more models measuring on the case's images.
The role lives in English
260 active tech ads in Spain mention computer vision (Shakers market analysis, n=31,957, September 2026). The title is English across the Spanish offer; your engagement, the visual system end to end.
Recomposition does not erase you
Four of every five mentions of computer vision in the ads travel as a skill of AI engineers and data scientists. The differential foundation models cannot erase: pixel, calibration, edge and the industrial case.
Arriving from another craft
Frequent doors: the C++ backend left with the camera, the data scientist who started detecting, the embedded developer who received the model. Solving pixels outside the course is half the battle.
Who you share the visual system with
A system that sees does not live alone: whoever interprets what got detected and whoever builds the device cross every engagement.
AI Engineer
The generative fusion point: when the visual system joins an agent that interprets what got detected, their row builds the layer on foundation models.
Data Scientist
The question before the camera: when the case needs experimental design besides vision, their row defines what the image must answer.
IoT Developer
The device your model lives in: sensors, communication and firmware. When deployment happens on the machine, their row connects hardware and pixel.
Python Developer
The language of training and the prototype: when detection already works and the application around it is missing, their row builds the product.
Backend Developer
The system that serves what got detected: when the visual case scales and the API and the data behind it are missing, their row builds what the camera feeds.
Boundaries between these roles get agreed per project: the job title does not set them.
Take the next step with Shakers
Want to build systems that see?
Pass Shakers certification and choose which visual cases get your judgement.
