Service · Web development

Hire a Python developer with team backup.

A Django or FastAPI developer working full-time on your project, with a senior colleague behind the scenes for code review and continuity. Not a loose freelancer you hope doesn't fall ill, but a dedicated developer within a Dutch team.

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Not a freelancer. A dedicated developer with a safety net.

When you look for a Python developer, the first instinct is often a freelancer through a platform. Cheaper per hour, available quickly, done. Until that freelancer falls ill, gets a better offer, or leaves code in a state nobody else understands. At that point your project stands still, and the original hourly saving has long since evaporated into remedial work and lead time.

We supply a Python developer as part of our web development team: full-time on your project, but with code review by a senior colleague, a replaceable position within the team, and a contract that legally secures the arrangement. Your code is delivered in a transferable form: documented, tested, and with a runbook that someone else can follow. That is the difference between "hiring a Python developer" and "adding Python capacity to your organisation".

We work in the Netherlands, in Dutch time zones, with communication in Dutch or English, whichever you prefer. No offshore handovers at 7 a.m., no language barriers in stand-ups, no half-day handover cycles when a production incident strikes. If something goes wrong at four o'clock on a Friday, someone from our team is on hand, not a ticket number in a queue.

The kinds of projects we excel at: data-heavy backend work, ETL and integration work, Django or FastAPI applications with real users, and AI services that touch production. For pure data science or research work, we'll honestly point you elsewhere, as other specialists are better suited. But for anything that has to go to production and keep running, we're right in our comfort zone.

What our Python developers build.

Three main areas where Python shines and where we have experience.

Direction 01

Django and FastAPI web applications

Full web applications built on Django for data-heavy admin tools and internal portals, or FastAPI microservices for modern REST and GraphQL APIs. Includes authentication, role management, audit logging and integrations with your existing systems, such as ERP, CRM, payment providers or an in-house data warehouse. Often combined with a React front end or a server-rendered Django front end, depending on what suits your team and user group. For Django-specific projects, particularly if you're weighing up whether hiring a Django developer is the right route, we put forward a developer who works with the Django ecosystem every day: ORM internals, querysets, custom middleware, Django REST framework, Celery integration and async views.

DjangoFastAPIREST & GraphQLPostgreSQLSSO & roles

Direction 02

Data pipelines, ETL and integrations

Python has become the standard for data work. We build ETL pipelines with Airflow or Prefect, schedulers with Celery, and integration layers between ERP systems, data warehouses and BI tools. Including monitoring, retry logic and clear alerts when a feed stalls. Well suited to organisations that want to consolidate data from multiple sources for analysis or operational reporting. We often see this need framed as hiring an ETL developer: the scope is then a data pipeline between a source system and a target system, with transformation logic and clear guarantees on data quality. We deliver this independently or as part of a broader software development project.

AirflowCelerypandasSQLAlchemydbt

Direction 03

AI and machine learning services

Custom AI services built on OpenAI, Anthropic or open-source models, with Python as the orchestration layer. Think classification services, embedding search with pgvector, document pipelines, or fine-tuning your own models on your data. We focus on production-ready ML services, not research notebooks that never get deployed. If you need a Python application built where AI is a core component, we deliver the full chain: data processing, model orchestration, an evaluation harness, monitoring and token-level cost control. If you want to hire a Python developer specifically for AI work, this is where our experience comes from.

OpenAI & Anthropic APIpgvectorPyTorch basicsHugging FaceLangChain

What you get at the end.

More than a developer for the duration of the project: a delivered codebase that can carry on independently.

Production codebase

Python code in your own Git repository, with tests, type hints and a CI/CD pipeline.

Architecture overview

A document explaining how the code is structured and why, useful for onboarding a successor.

Operations runbook

How to run a release, how to triage an incident, where the logs live, and what each alert means.

Knowledge transfer

Live handover to your own team or successor, with video recordings and walkthrough sessions.

Option for ongoing maintenance

For when you want a dedicated hand after handover for security patches and small further development.

Three ways of working, choose what suits you.

How you deploy our Python developer depends on what you already have in-house and how large the scope is.

Engagement model 01

Dedicated developer

Our Python developer works full-time on your project, integrates with your stand-ups and your product owner, but works from our team for reviews and sparring. You steer on output, we handle the continuity. Suitable when you don't yet have Python capacity in-house or need to run a short peak-period project.

Engagement model 02

Team augmentation

Your own developers lead the project; our Python specialist joins alongside them for the parts where your team has less experience — Django internals, an ML component, a complex data pipeline. We work in your repository, your rituals, your tooling.

Engagement model 03

Project-based scope

You commission a defined assignment, we deliver a working product within an agreed scope. Fixed contract, fixed deliverables, handover to your team. Suitable for MVPs, integration projects or clearly bounded back-end builds.

Engagement model 04

Senior lead for your team

A senior Python developer who also thinks at architecture level: setting code standards, mentoring juniors, approaching technical debt. Often requested when an organisation wants to grow from a handful of loose scripts into a structured Python codebase, or when an earlier freelance project has left code nobody dares to touch any more. We bring structure without rewriting everything.

How an engagement works.

01Introduction→ 02Match→ 03Build→ 04Handover

Introduction

A short session to sharpen the scope, stack and preferred way of working. We share examples of previous work and discuss seniority level.

Developer match

We match a Python developer to your project based on the stack mix your scope requires. You meet the developer in advance yourself.

Sprints with reviews

Two-week sprints with demo and review. Every PR goes through code review by a second senior — no code reaches main without four eyes on it.

Handover

At the end of the engagement, a structured handover to your own team or a successor, with a runbook and walkthroughs.

Stack expertise from our Python team.

We work at mid-level to senior lead level. No junior-only staffing: every project has at least one senior behind the code review. The stack mix you choose depends on your project; the pills below are what we build with in practice.

Web & API

DjangoFastAPIFlaskDjango REST frameworkGraphQL (Strawberry)

Data & ML

pandasNumPySQLAlchemyCeleryAirflowPyTorch basicsTensorFlow basics

Infrastructure & cloud

DockerKubernetesAWSGCPTerraformGitHub Actions

Where Python truly excels.

Four scenarios where we choose Python over other languages — and, to be honest, a few scenarios where we would not recommend it.

Scenario 01

Data-heavy back-end

When your application performs a lot of data transformation — aggregations, joins, custom reporting — Python with pandas or SQLAlchemy is almost always the faster development route compared with equivalent work in Node or Java.

Scenario 02

AI and LLM integrations

The Python ecosystems around OpenAI, Anthropic, Hugging Face and LangChain are ahead of those in other languages. For production-ready AI features, we almost always reach for Python — often combined with FastAPI for the service layer.

Scenario 03

Internal tools and admin portals

Django admin delivers a working back office within a sprint, provided your data model is clear. For internal tools where UX perfection isn't the top priority, this saves an enormous amount of lead time. For customer-facing interfaces, we usually combine it with a React or Node.js layer.

Scenario 04

Automation and scraping work

For scheduled jobs, web scraping, document processing and system integration, Python has been the workhorse choice for years. Quick to write, easy to maintain, and backed by by far the largest library ecosystem.

When something else makes more sense

Real-time and low-latency

For real-time chat, high-frequency WebSocket layers or latency-critical micro-services, we tend to look at Node.js or Go first. Python can do it, but it is rarely our first choice there.

When something else makes more sense

Full-stack TypeScript projects

When your front end is already React/Next.js and the back-end logic remains limited, a TypeScript back end is often more practical than a separate Python stack — less context-switching, shared types. Our full-stack developers are then the better choice.

Why an agency developer rather than a freelancer.

For a one-off project with a short scope and limited risk, a freelancer is fine. For anything that touches production, integrates with other systems, or runs longer than a few weeks, we advise against it. The reasons aren't abstract: we see them come up every month in takeover projects.

Code review by a second senior. Every pull request we write goes through peer review. A freelancer reviews their own work or, at best, someone on your team who happens to know a bit of Python. The difference in code quality after six months is significant.

Replaceable within the team. When our developer is off sick for two weeks or moves to another assignment, they actively hand over to a colleague who already knows the codebase from reviews. A freelancer goes on holiday, and you're left at a standstill, or worse, you have to get someone up to speed halfway through.

Contractual certainty. You contract with Appfront B.V., not with an individual. That means IP rights are properly arranged, NDAs are enforceable, and there is a liable company should anything go wrong. For projects involving your customer data or business data, that is not a luxury.

Handover-ready code. We know the engagement will eventually end, so we write code with that handover in mind. Tests, type hints, documentation. No "only Frank knows how it works" hidden in a script. You can also combine this with our software development service for longer engagements where several disciplines come together.

Frequently asked questions.

What is the minimum lead time to bring in a Python developer?

For a team augmentation role we can often have someone start within a few weeks, depending on the required stack mix and seniority. For a project-based engagement with a scoping phase it takes longer, because we first produce a scope document and plan. We are honest during the introductory conversation about what is realistic.

Contract: fixed or flexible?

Both. For a dedicated developer and team augmentation we work with an hourly rate and a minimum weekly commitment, cancellable monthly. For project-based scope we agree a fixed contract based on a scope document and delivery. No long lock-ins and no mandatory minimum contract term running into years.

Do you work with a Dutch team or an EU team?

The Python team works from the Netherlands in the Dutch time zone. Communication can be in Dutch or English, whichever you prefer. No offshore handover, no 24-hour handover cycles, no language barriers in code review.

What if the developer leaves halfway through the project?

That is precisely why we work with code review by a fellow senior. When someone leaves, the second senior is already in the codebase and can take over after a short handover. So you are not left standing still. The same applies in the event of illness or extended absence.

How is knowledge handed over at the end?

Standard part of every engagement: a runbook with operational instructions, an architecture document explaining the choices made, and walkthrough sessions with your own team or successor. Preferably in the final sprints of the engagement, not on the very last day.

How is pricing determined?

For team augmentation and dedicated developers we charge an hourly rate depending on seniority (mid-level, senior, senior-lead). For project-based scope we charge per sprint or at a fixed total budget based on a scope document. We share our rates openly during the introductory call, with no hidden tiers or penalty clauses.

Do you also do ETL and data pipeline work separately from app development?

Yes. A considerable part of our Python work is ETL, data integration and pipeline development, often built around Airflow or Prefect, with output to a data warehouse or operational system. This can be a standalone project or part of a larger app build.

Do you have experience with machine learning and AI services in production?

Yes, with a focus on production. We don't do pure research, but we do build production-ready AI services: classification, embedding search, document pipelines, LLM integrations via OpenAI or Anthropic, and fine-tuning where it makes sense. For deeper ML research, we often work alongside a client's data science team.

Talk to us about your Python project.

A no-obligation introductory call of half an hour. We listen to your scope, ask follow-up questions where needed, and are honest about whether a Python developer is the right route here, or whether a different stack would suit you better. No sales patter.

Schedule an introductory call Email fabian.vandijk@appfront.nl