Python API and service engineers
HTTP APIs, workers, and auth in FastAPI or Django, typed enough to survive the next route, not a views.py that became the product.
Python · APIs · Data · ML
Python developers who ship services, not notebooks
A Python hire is not “someone who ran a Jupyter notebook.” iQud engineers ship product APIs, workers, and data-adjacent services in your repo (contracts, pipelines, and models that survive the next feature), not a script that only works on their laptop.
- Python APIs and workers
- Data pipelines as product work
- ML that reaches production
- First PR in about a week
Python delivery signals
- 5 dTypical time to first pull request
- APIProduct services, not notebook demos
- DataPipelines treated as owned product work
- 2 wkSprint cadence with service review
The Python seats we actually staff
“Python” is not one job. We match on the service your product needs: the same stacks behind iQud’s live Python, Data Engineering, and ML Development pages.
01 02 Data-adjacent Python
Pipelines, jobs, and transforms that land in your warehouse or Postgres, owned like product work, not a cron that nobody wants.
03 ML that ships past the notebook
Training, serving, and the boring glue around a model, so the next quarter’s feature can call it without a research intern on call.
04 Automation that stays in the repo
Internal tools, batch jobs, and integrations treated as product surfaces: with tests, retries, and an owner in standup.
05 Data layers the next job can live with
Postgres schemas, warehouse tables, or document stores designed so the next pipeline does not fight last quarter’s column names.
06 When Node, Nest, or a mixed backend seat is better
A TypeScript API fleet or a Rails admin still wants a different hire. We will say so on the intro call instead of forcing a Python-only seat.
Tools they open on day one
Every tile is a live iQud technology or service page. The strip below is the backend catalog around Python, the same domain these engineers already ship.
Back End
From intro call to a merged PR
A Python hire should be shipping a route, a job, or a pipeline in your repo, not sitting in a two-month onboarding theatre while the service contract stays a rumour.
1 Map the Python gap
API vs pipeline vs ML, FastAPI vs Django, seniority, overlap hours, and what “done in 30 days” looks like in the service layer.
2 Shortlist real engineers
We match available Python specialists to your brief and share relevant API, worker, pipeline, or production-ML work.
3 You interview
Meet the human who will join standup. Validate how they talk through a failed job, a schema change, and the last notebook they refused to leave in production.
4 First sprint in your tools
Repo access, environments, 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 Python engineer first. A pair or a pipeline split only when the service work actually needs it.
One embedded engineer
A Python specialist joins your squad, takes direction from your lead, and works in your rituals.
Best forClosing an API or pipeline velocity gap without a new vendor process
Dedicated Python seat
A stable owner for the service, a job fleet, or the data-adjacent layer, with senior review on the sprint.
Best forA product that needs a named Python owner
Scoped Python initiative
A defined slice: FastAPI cutover, a warehouse pipeline, or an ML serving path with contracts already in motion.
Best forA milestone you can point at, not an open-ended bench
Python hiring rates, in writing
Two ways to staff a Python 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 Python capacity for API spikes, reviews, and scoped tickets. You only pay for hours worked.
- Same Python engineers as a monthly seat
- Best for overflow, a job fix, or a single pipeline
- 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 Python engineer on your sprint, about 160 hours of dedicated service 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, environments, and standup
- API, worker, and pipeline 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 Python engineers (APIs, workers, data-adjacent services). 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 Python here
A mediocre Python developer produces a notebook that works on their laptop. These engineers produce a service that survives real traffic, real data, and your next quarter of jobs.
Services and pipelines, not “Python who can import pandas”
They live in FastAPI routes, Celery/RQ workers, and why last week’s 500s came from a job that should have been idempotent.
Your repo, your cloud, your hours
GIFT City overlap with Europe and the US. Service reviews happen live when your leads are online.
Senior eyes on the sprint
Mid-level speed without unsupervised schema drift. 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.
Python hiring questions
Start with one. Most clients embed a single senior or mid-level Python engineer, then add a pair if the API or pipeline backlog justifies it.
All three when they are product work in Python. An API seat is FastAPI or Django services. A data seat is pipelines and jobs. An ML seat is models that reach production. We will only shortlist engineers on stacks we already ship.
That is a backend hire, not this seat. Say so on the intro call and we will not force a Python-only profile onto a mixed service bench.
After we map the role and you approve the hire, first pull requests typically land within a week, faster when the repo and environments are ready.
Yes. GitHub, Jira, your cloud, 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 Python 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 Python engineer who can ship the service
Tell us API vs pipeline vs ML, the data layer, and the first job you want in production. We’ll come back with a named profile, a start window, and a two-week plan.