Production ML engineers
Training, eval, serving, and the boring glue around a model, so the next quarter’s feature can call it without a research intern on call.
AI · ML · NLP · Computer Vision
AI / ML engineers who ship production intelligence
An AI / ML hire is not “someone who trained a Colab notebook.” iQud engineers ship generative features, models, NLP, and computer vision that survive eval, serving, and the next dataset, not a demo that never left the laptop.
- Models that reach production
- NLP and computer vision as product work
- Generative features with eval, not vibes
- First PR in about a week
AI / ML delivery signals
- 5 dTypical time to first pull request
- ProdServing and eval, not notebook demos
- 4 surfacesGenAI, models, NLP, and vision
- 2 wkSprint cadence with model review
The AI / ML seats we actually staff
“AI / ML” is not one job. We match on the product surface you need: the same stacks behind iQud’s live AI Development, ML Development, Computer Vision, and NLP pages.
01 02 Generative features that survive eval
RAG, prompts, and LLM product slices treated as owned work (with retrieval quality, fallbacks, and cost), not a chatbot demo that only works on one PDF.
03 NLP as a product surface
Extraction, classification, search, and language workflows owned in the repo, not a sentiment script nobody can retrain after launch.
04 Computer vision in the workflow
Detection, OCR, and imaging intelligence that run on real inputs, not a YOLO notebook that only works on the engineer’s sample folder.
05 MLOps the next job can live with
Pipelines, versioning, and monitoring treated as product work, not a “we’ll productionize it later” slide.
06 When a Python API, prompt-only, or CV-only seat is better
A FastAPI fleet with no model, a RAG chatbot with no training loop, or a pure imaging bench still wants a different hire. We will say so on the intro call instead of forcing a mixed AI / ML seat.
Tools they open on day one
Every tile is a live iQud technology or service page. The strip below is the AI / ML catalog these engineers already ship (TensorFlow, PyTorch, OpenAI, Hugging Face, OpenCV, and the rest of the production stack), not a backend or frontend dump.
From intro call to a merged PR
An AI / ML hire should be shipping a model, an eval, or a generative slice in your repo, not sitting in a two-month onboarding theatre while the notebook stays a rumour.
1 Map the AI / ML gap
Models vs NLP vs vision vs generative features, data readiness, seniority, overlap hours, and what “done in 30 days” looks like in serving or eval.
2 Shortlist real engineers
We match available AI / ML specialists to your brief and share relevant production-model, NLP, vision, or generative-feature work.
3 You interview
Meet the human who will join standup. Validate how they talk through a failed eval, a serving regression, and the last model they actually owned past the notebook.
4 First sprint in your tools
Repo access, data 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 AI / ML engineer first. A pair or a surface split only when the work actually needs it.
One embedded engineer
An AI / ML specialist joins your squad, takes direction from your lead, and works in your rituals.
Best forClosing a production-intelligence gap without a new vendor process
Dedicated AI / ML seat
A stable owner for the model, the NLP surface, or the generative slice, with senior review on the sprint.
Best forA product that needs a named intelligence owner
Scoped AI / ML initiative
A defined slice: a first production model, an NLP extraction path, or a generative feature with contracts already in motion.
Best forA milestone you can point at, not an open-ended research bench
AI / ML hiring rates, in writing
Two ways to staff an AI / ML 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 AI / ML capacity for feature spikes, evals, and scoped tickets. You only pay for hours worked.
- Same AI / ML engineers as a monthly seat
- Best for overflow, a model 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 AI / ML engineer on your sprint, about 160 hours of dedicated production-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, data paths, and standup
- Model, NLP, vision, or generative 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 AI / ML engineers (production models, NLP, computer vision, generative features). 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 AI / ML here
A mediocre AI / ML developer produces a notebook that works on their laptop. These engineers produce a feature that survives real data, real eval, and your next release.
Production work, not “who can train a Colab notebook”
They live in serving, eval, and why last week’s regression came from a data leak that should have owned its own check.
Your repo, your data, your hours
GIFT City overlap with Europe and the US. Model 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.
AI / ML hiring questions
Start with one. Most clients embed a single senior or mid-level AI / ML engineer, then add a pair if the model, NLP, or generative backlog justifies it.
All four when they are product work. An ML seat is training, eval, and serving. An NLP seat owns language workflows. A vision seat owns detection and OCR. A generative seat owns RAG and LLM product slices with eval. We will only shortlist engineers on stacks we already ship.
That is a Python hire, or a GenAI-product seat, not this mixed production AI / ML profile. Say so on the intro call and we will not force a model-training seat onto an API or prompt-only brief.
After we map the role and you approve the hire, first pull requests typically land within a week, faster when the repo, data 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 AI / ML 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 an AI / ML engineer who can ship past the notebook
Tell us models vs NLP vs vision vs generative features, and the first thing you want in production. We’ll come back with a named profile, a start window, and a two-week plan.