Engineering Guadalajara · in person Full timeYou turn the cameras a client already has from video into decisions: what happened, where, at what time, with the clip that proves it.
The video stays at the client's site.The roleTrain and tune detection, segmentation and tracking models on real video.Make them run at the client's site, on cameras and recorders that already exist.Turn what the model sees into events with time, place and a video clip.Measure false positives and negatives with the client and decide which threshold can be signed off.Your first 90 daysDay 30You run the current vision pipeline on test video and deliver a measured improvement.Day 60You own a complete case:



from the camera feed to the alert the shift lead receives.Day 90A model of yours runs on a client's cameras with its error rate documented.Stack and toolsPython PyTorch YOLO OpenCV ffmpeg RTSP / ONVIFWhat we look forMust haveYou have trained or tuned a vision model and you know why it fails.Fluent Python and the judgment to measure, not just run examples.You understand a camera's limits: light, angle, resolution, compression.You write code that runs the same on your machine as on a client's hardware.Nice to haveExperience with YOLO, ByteTrack or other trackers.Video over RTSP or ONVIF, DVR and NVR recorders.Optimizing models for hardware without a big GPU.What working here is likeWhat you build goes into products that operate real organizations.
You work directly with the founders, with no layers between your code and production, under the same rule we give the intelligence: a person signs what is critical.
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📌 Computer Vision (Jalisco)
🏢 Nuvnext
📍 Jalisco