Detection that survives real cameras
Bounding boxes, classes, and thresholds treated as product work, not a YOLO notebook that only works on the engineer’s sample folder.
Computer Vision · Detection · OCR · Imaging
Computer vision engineers who ship on real images, not samples
A computer vision hire is not “someone who ran a YOLO notebook.” iQud engineers ship detection, OCR, and imaging intelligence that survive real cameras, real lighting, and the next dataset, not a demo that only works on the engineer’s sample folder.
- Detection and OCR on real inputs
- Imaging intelligence in the workflow
- Eval and serving past the notebook
- First PR in about a week
Computer vision delivery signals
- 5 dTypical time to first pull request
- CVReal images, not a sample-folder demo
- OCRDetection and extraction as product work
- 2 wkSprint cadence with image review
The computer vision seats we actually staff
“Computer vision” is not one job. We match on the product surface you need: the same stacks behind iQud’s live Computer Vision, ML Development, and MLOps pages.
01 02 OCR and document imaging
Extraction from scans, photos, and forms owned in the repo, not a one-shot Tesseract script nobody can retrain after launch.
03 Imaging intelligence in healthcare and enterprise
Visual analytics in the workflow (quality, privacy, and review), not a demo that only runs on one GPU laptop.
04 Serving and eval past the notebook
Inference, latency, and failure cases owned like product work, so the next camera feed does not fight last quarter’s weights.
05 MLOps the next job can live with
Datasets, versioning, and monitoring treated as owned work, not a “we’ll productionize it later” slide.
06 When a GenAI, mixed AI / ML, or Python API seat is better
A RAG chatbot, a training-and-NLP mix, or a FastAPI fleet with no images still wants a different hire. We will say so on the intro call instead of forcing a vision-only seat.
Tools they open on day one
Every tile is a live iQud technology or service page. The strip below is the computer vision stack these engineers already ship (OpenCV, YOLO, PyTorch, TensorFlow, and the serving tools around them), not LangChain, chatbots, or the full AI / ML catalog.
From intro call to a merged PR
A computer vision hire should be shipping a detector, an OCR path, or an eval on your images, not sitting in a two-month onboarding theatre while the sample folder stays a rumour.
1 Map the computer vision gap
Detection vs OCR vs imaging analytics, data readiness, seniority, overlap hours, and what “done in 30 days” looks like on real inputs.
2 Shortlist real engineers
We match available CV specialists to your brief and share relevant detection, OCR, or imaging-intelligence work.
3 You interview
Meet the human who will join standup. Validate how they talk through a lighting failure, a false positive, and the last model they actually owned past the notebook.
4 First sprint in your tools
Repo access, image paths, and a first pull request, typically inside a week once you say go.
Start with one seat. Grow if the backlog says so.
Most clients embed a single computer vision engineer first. A pair or a surface split only when the work actually needs it.
One embedded engineer
A CV specialist joins your squad, takes direction from your lead, and works in your rituals.
Best forClosing a detection or OCR gap without a new vendor process
Dedicated computer vision seat
A stable owner for the detector, the OCR path, or the imaging pipeline, with senior review on the sprint.
Best forA product that needs a named vision owner
Scoped computer vision initiative
A defined slice: a first production detector, an OCR extraction path, or an imaging workflow with contracts already in motion.
Best forA milestone you can point at, not an open-ended research bench
Computer vision hiring rates, in writing
Two ways to staff a computer vision engineer. Hourly for spikes and defined tickets. A dedicated monthly seat when you want someone in your standup every day, at a lower effective rate than running the clock.
Hire by the hour
$20/ hour
Flexible computer vision capacity for feature spikes, evals, and scoped tickets. You only pay for hours worked.
- Same computer vision engineers as a monthly seat
- Best for overflow, a detector fix, or a single release
- Start fast, pause when the spike is done
- Billed against actual hours, not a retainer
Best value
Hire for a month
$2,000/ month
A named computer vision engineer on your sprint, about 160 hours of dedicated imaging-intelligence capacity, with senior review in the cadence.
A full-time month at $20 is $3,200. This seat is $2,000.
- Named engineer in your repo, image paths, and standup
- Detection, OCR, or imaging work in the same engagement
- Month-to-month. No annual lock-in.
- Lower cost than the same hours billed at $20
Rates are for dedicated computer vision engineers (detection, OCR, imaging intelligence, production serving). Seniority mix and overlap hours are confirmed on the intro call. We will not quote a stack we do not already ship.
Why product teams staff computer vision here
A mediocre CV developer produces a notebook that works on their sample folder. These engineers produce a feature that survives real cameras, real lighting, and your next release.
Production work, not “who can run a YOLO notebook”
They live in false positives, lighting, and why last week’s miss came from a distribution shift that should have owned its own check.
Your repo, your images, your hours
GIFT City overlap with Europe and the US. Image reviews happen live when your leads are online.
Senior eyes on the sprint
Mid-level speed without unsupervised model debt. Review is part of the engagement, not an extra SKU.
Ten clients a quarter, on purpose
We do not run a revolving bench. Capacity is limited so the engineer you interview is the one in standup.
Computer vision hiring questions
Start with one. Most clients embed a single senior or mid-level CV engineer, then add a pair if the detection, OCR, or imaging backlog justifies it.
All three when they are product work. A detection seat owns boxes, classes, and thresholds. An OCR seat owns extraction from scans and photos. An imaging seat owns visual analytics in healthcare or enterprise workflows. We will only shortlist engineers on stacks we already ship.
That is a GenAI hire, an AI / ML hire, or a Python hire, not this vision seat. Say so on the intro call and we will not force a detector profile onto a RAG or API-only brief.
After we map the role and you approve the hire, first pull requests typically land within a week, faster when the repo, image paths, and serving environment are ready.
Yes. GitHub, Jira, your CI, your standups. We do not invent a parallel process unless you ask for one.
Hourly ($20) is for spikes and defined tickets. You pay only for hours worked. The monthly seat ($2,000) is a named computer vision engineer on your sprint, about 160 hours of dedicated capacity. The same month billed hourly would be $3,200. Seniority and overlap hours are confirmed on the intro call.
Give the product a computer vision engineer who can ship past the notebook
Tell us detection vs OCR vs imaging, and the first thing you want running on real inputs. We’ll come back with a named profile, a start window, and a two-week plan.