RAG engineers
Chunking, retrieval, citations, and fallbacks owned in the repo, not a chatbot demo that only works on one PDF.
GenAI · RAG · Prompts · LLM apps
GenAI developers who ship LLM product features
A GenAI hire is not “someone who pasted a ChatGPT prompt.” iQud engineers ship RAG, prompt systems, chatbots, and generative product slices that survive eval, retrieval quality, and cost, not a playground demo that only works on one PDF.
- RAG with retrieval you can measure
- Prompt systems, not a single string
- Chatbots and summarization as product work
- First PR in about a week
GenAI delivery signals
- 5 dTypical time to first pull request
- RAGRetrieval and eval, not a playground paste
- LLMProduct features, not a one-PDF demo
- 2 wkSprint cadence with output review
The GenAI seats we actually staff
“GenAI” is not one job. We match on the product surface you need: the same stacks behind iQud’s live AI Development, Text Generation, Prompt Engineering, and RAG Chatbots pages.
01 02 Prompt systems, not a single string
Templates, tools, and eval treated as product work, so last week’s prompt change has a test, not a Slack screenshot.
03 Chatbots and assistants that survive real users
Guards, handoff, and conversation state as owned work, not a playground thread nobody can reproduce after launch.
04 Text generation as a product slice
Summarization, drafting, and rewrite flows with cost and quality checks, not an unbounded “just call the model” button.
05 Eval and cost the next job can live with
Offline checks, tracing, and spend treated as product work, not a “we’ll measure it later” slide.
06 When a training, CV, or Python API seat is better
A PyTorch training loop, a detection bench, or a FastAPI fleet with no LLM still wants a different hire. We will say so on the intro call instead of forcing a GenAI-only seat.
Tools they open on day one
Every tile is a live iQud technology or service page. The strip below is the LLM application stack these engineers already ship (OpenAI, LangChain, Hugging Face, and the chatbot tools around them), not TensorFlow, OpenCV, or the full AI / ML catalog.
From intro call to a merged PR
A GenAI hire should be shipping a retrieval path, a prompt change, or a generative slice in your repo, not sitting in a two-month onboarding theatre while the playground thread stays a rumour.
1 Map the GenAI gap
RAG vs prompts vs chat vs summarization, data sources, seniority, overlap hours, and what “done in 30 days” looks like in eval or a first production path.
2 Shortlist real engineers
We match available GenAI specialists to your brief and share relevant RAG, prompt-system, chatbot, or generative-feature work.
3 You interview
Meet the human who will join standup. Validate how they talk through a retrieval miss, a cost spike, and the last prompt they actually owned past the playground.
4 First sprint in your tools
Repo access, model keys, 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 GenAI engineer first. A pair or a surface split only when the work actually needs it.
One embedded engineer
A GenAI specialist joins your squad, takes direction from your lead, and works in your rituals.
Best forClosing an LLM-product gap without a new vendor process
Dedicated GenAI seat
A stable owner for RAG, the prompt system, or the chatbot, with senior review on the sprint.
Best forA product that needs a named generative owner
Scoped GenAI initiative
A defined slice: a first RAG path, a summarization flow, or a chatbot with contracts already in motion.
Best forA milestone you can point at, not an open-ended prompt sandbox
GenAI hiring rates, in writing
Two ways to staff a GenAI 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 GenAI capacity for feature spikes, evals, and scoped tickets. You only pay for hours worked.
- Same GenAI engineers as a monthly seat
- Best for overflow, a RAG 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 GenAI engineer on your sprint, about 160 hours of dedicated LLM-product 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, model keys, and standup
- RAG, prompts, chat, or generation in the same engagement
- Month-to-month. No annual lock-in.
- Lower cost than the same hours billed at $20
Rates are for dedicated GenAI engineers (RAG, prompt systems, chatbots, generative product 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 GenAI here
A mediocre GenAI developer produces a playground thread that works on their laptop. These engineers produce a feature that survives real retrieval, real eval, and your next release.
Product work, not “who can paste a ChatGPT prompt”
They live in retrieval quality, eval, and why last week’s hallucination came from a chunking choice that should have owned its own check.
Your repo, your models, your hours
GIFT City overlap with Europe and the US. Output reviews happen live when your leads are online.
Senior eyes on the sprint
Mid-level speed without unsupervised prompt 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.
GenAI hiring questions
Start with one. Most clients embed a single senior or mid-level GenAI engineer, then add a pair if the RAG, prompt, or chatbot backlog justifies it.
All four when they are product work. A RAG seat owns retrieval and citations. A prompt seat owns templates, tools, and eval. A chatbot seat owns conversation state and guards. A generation seat owns summarization and drafting with cost checks. We will only shortlist engineers on stacks we already ship.
That is an AI / ML hire, or a Python hire, not this LLM-application seat. Say so on the intro call and we will not force a RAG profile onto a training 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, data sources, and model keys 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 GenAI 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 GenAI engineer who can ship past the playground
Tell us RAG vs prompts vs chat vs summarization, and the first thing you want in production. We’ll come back with a named profile, a start window, and a two-week plan.