Web + API platforms
Django, FastAPI, Flask, Litestar: REST + GraphQL + gRPC services with idempotent endpoints, contract tests, and per-route SLOs.
Hire Python engineers · Amsterdam
Hire senior Python engineers in Amsterdam.
Senior Python engineering for production systems: Django, FastAPI, data pipelines, ML inference, and automation that survives the operating cutover.
- In production
- 2018+
- Senior bench
- 40+ G6/G9 engineers
- Floor
- G6+ on client work
- Europe delivery model
- Senior bench + in-market workshops
Hiring Python engineers in Amsterdam
Senior Python delivery, anchored in Amsterdam.
Amsterdam, Netherlands engagements ship with the same seniorPython bench used across every Prosigns delivery. No staff-augmentation, no junior fallback, no SDR routing. The engineers in the proposal are the engineers in production.
- Europe delivery model
- Senior bench + in-market workshops
- Operating model
- US/EU overlap for architecture calls
- Engineering bench
- Senior-only teams
- Reply window
- <2 hr GMT / CET
Why senior, not contractor
Python in production is a different problem than Python on a laptop.
Most Python engagements ship as scripts that ran once on a developer's laptop and never made it into a managed runtime. Performance falls over at the second percentile, the test pyramid is upside-down, and the dependency tree silently bumps a major version. Prosigns ships Python as production substrate: typed where it matters, observed everywhere, packaged into reproducible runtimes, and deployed against an SLO the operations team actually defends.
Senior floor
G6+ minimum
Bench depth
40+ G6/G9 engineers
In production
2018+
Engagement
Outcome-led SOW
Where Python ships
6 sub-areas, grounded in shipped engagements.
Specific applications of Python we’ve built and operate. Every example below maps to a real engagement, not a bullet on a stack-card.
01 02 Data engineering pipelines
Airflow, Prefect, Dagster, dbt: orchestrated ELT, data quality monitoring, lineage, and reconciliation against the SoR.
03 ML inference + serving
FastAPI + Ray Serve / Triton / BentoML for production model serving. Batch + streaming inference, eval harnesses, drift monitoring.
04 Async + event-driven
asyncio, Celery, RQ, Faust: durable task queues, event consumers, replay-safe workers with DLQ wiring into incident response.
05 Internal tooling + automation
Operational scripts hardened into managed runtimes: typed, logged, alerted, and runbook'd. The opposite of cron-job-nobody-owns.
06 Replatforming legacy Python
Python 2.7 → 3.x migrations, Flask 1 → modern, custom apps off Plone / Pyramid onto FastAPI or Django, with strangler-fig discipline.
Stack depth
The Python ecosystem, owned.
Frameworks, libraries, and runtime tools the bench has shipped in production. Not a CV-skim, a working depth.
Web frameworks
- Django
- FastAPI
- Flask
- Litestar
- Starlette
- Tornado
Data + orchestration
- Airflow
- Prefect
- Dagster
- dbt
- Pandas
- Polars
- DuckDB
ML + inference
- PyTorch
- scikit-learn
- Ray
- Triton
- BentoML
- MLflow
Async + queues
- asyncio
- Celery
- RQ
- Dramatiq
- Faust
- AnyIO
Quality + ops
- pytest
- mypy
- ruff
- Pydantic
- OpenTelemetry
- Sentry
Engagement models
Three ways to engage Python engineers.
We don’t bill hourly contractors. Engagements run against outcomes, choose the shape that matches the work.
See engagement models- 01
Fixed-scope
Defined deliverable, fixed price
When the deliverable is clear and the scope is bounded: an MVP, a migration, a discrete platform build. Senior engineering against a written outcome, not against a body count.
- 02
Embedded squad
Senior product team, ongoing
When the work is product-shaped and the cadence is continuous. A senior pod (engineering + design + PM as needed) embedded into your team, with the practice lead co-piloting from HELM.
- 03
Managed services
Steady-state operations
When the system is running and needs ongoing engineering ownership: operations, SLO defense, release management, security and compliance evidence. Monthly retainer against a published SLA.
Selected work
Python engineering that shipped.
Financial services
Fraud-detection API platform on FastAPI for a regional bank.
99.97%uptime SLO defendedReplaced a Flask + Celery monolith with FastAPI services and Ray Serve for model inference. Sub-50ms p99 latency on the scoring path. Survived the first regulatory examination on the new stack.
Duration · 7 months
Brief us
Reply < 4 business hoursHave a Python workload? Send a brief.
Five fields. Goes straight to the practice lead, not an SDR. We’ll reply with a senior engineer’s read on fit, scope, and the engagement model that suits the work.
FAQ
Questions buyers ask.
Everything below also appears in the proposal and the SOW: no surprises after signing.
Django, FastAPI, or Flask?
Django wins for full-stack apps with admin, auth, and content where the batteries-included story is the spine. FastAPI wins for typed APIs at scale where async + Pydantic make the contract the source of truth. Flask is fine for legacy maintenance but rarely the first choice for new builds. We'll tell you which fits your workload.
Do you deploy on Lambda, ECS, EKS, or Vercel?
All four are in our portfolio depending on the workload. Lambda for spiky/burstable APIs and event consumers, ECS / EKS for steady-state services, Vercel for Next.js + Python edge functions. The pick is workload-led, not vendor-led.
Is this staff-aug, or is it engineering-led delivery?
Engineering-led delivery. We don't bill hourly contractors against your JIRA board. Every engagement runs against a defined outcome with a senior engineer accountable from kickoff to operating cutover. If you genuinely need staff-aug, discrete bodies, your management, hourly rates, we'll be honest and route you to a partner that fits.
What seniority floor are we talking about?
G6 minimum (six-plus years in their craft) on every billable hour. Department leads are G9 or G10. We don't flex juniors onto the bench mid-sprint, we don't subcontract to delivery centers, and we don't dilute senior rates with mixed staffing. The bench in the proposal is the bench in production.
How does pricing work?
Three engagement models published at /engagement-models/. Fixed-scope for defined deliverables, embedded squads for ongoing product work, managed services for steady-state operations. Rates depend on seniority, engagement length, and region. Discovery + scoping conversation is free; SOWs are written against deliverables, not bodies.
Where are your engineers based?
Senior-only across Dallas, Doha, Lahore, and Islamabad. We staff against the engagement's needs (timezone, language, regulatory frame), not against arbitrary regional preferences. Most engagements run with a US/EU-aligned core and a follow-the-sun extended bench when the workload warrants it.
Will the engineers I see in the proposal be the engineers shipping the work?
Yes. We name the engineers in the SOW, attach their profiles, and they're on the kickoff. We don't bait-and-switch with senior reviewers and junior execution. If a named engineer needs to roll off the engagement (rare), we surface a replacement from the same seniority tier with explicit handoff.
Talk to a Python lead
Senior Python engineering, accountable from kickoff.
Bring the workload: we’ll bring a senior engineer plus the practice lead most relevant to the work. 30 minutes, no obligation, no junior reps.