Python Development Services

PS3G provides Python development services for the work Python is best at: web backends that need to exist quickly, data pipelines, automation that removes a person from a repetitive task, and the glue between an application and a machine-learning model or a large language model. That covers a founder who wants a product API and an admin in weeks, an operations lead at a growing company whose reporting runs on scripts nobody dares to touch, and a data or IT group inside a larger organization that needs a model put into production properly. We build it, test it and either hand it over or run it.

Python Development Services: what we build and for whom

Python’s strength is breadth with a low cost of entry. The same language runs a Django site, a FastAPI microservice, a nightly ETL job, a scraper, a notebook that became a forecasting model and the service that now serves that model. As a Python development company we organize the work into four groups.

• Web backends and APIs. Django for applications that need an admin, an ORM, authentication and forms out of the box; FastAPI for lean, typed APIs and services that front a model or a queue.
• Data engineering. Pipelines that collect, clean and load data into a warehouse, scheduled and monitored, with tests on the data as well as the code. The platform side of that is covered under big data solutions.
• Automation. Scripts and small services that replace manual exports, reconciliations, report assembly and file handling, built so they fail loudly and can be re-run safely.
• AI and machine learning integration. Retrieval pipelines, model serving, evaluation harnesses and the API layer between an LLM and your application, in step with our AI development service.

Python is the wrong choice for some things, and we will tell you which. A high-throughput transactional core with strict latency, a Windows desktop application, a mobile app or a line-of-business system for a team that already runs everything on .NET will usually be better served by another stack. Our .NET development group handles the last of those, often with Python kept for the data and AI pieces alongside it.

What the service covers

Python software development at PS3G runs from architecture to maintenance. The table lists the typical work and our default tools; we follow your standards where you have them.

Type of work	What it looks like	Typical tools
Django development	Customer portals, internal applications, content-heavy products with an admin, permissions, forms and reporting	Django, Django REST Framework, Celery, PostgreSQL, Redis
FastAPI development	Typed REST services, model-serving endpoints, webhook receivers, backends for React and mobile apps	FastAPI, Pydantic, SQLAlchemy, Uvicorn, OpenAPI
Data pipelines	Batch and streaming ETL, warehouse loading, data quality checks, orchestration and alerting	Airflow, dbt, pandas, Polars, PySpark, BigQuery or Azure Data Factory
Automation and integration	Scheduled jobs, file and email processing, API-to-API sync, RPA-adjacent scripts with audit logs	Requests, httpx, Playwright, Azure Functions or AWS Lambda or Cloud Run
AI integration	Retrieval-augmented generation, prompt and evaluation pipelines, agents with tool access, guardrails and logging	LangChain or LlamaIndex, vector databases, OpenAI, Azure OpenAI, Gemini, Vertex AI
Machine learning	Forecasting, classification, anomaly detection, model packaging and serving, drift monitoring	scikit-learn, PyTorch, MLflow, ONNX
Quality and delivery	Typed code, tests, linting, dependency scanning, containers, pipelines	pytest, mypy, Ruff, Docker, GitHub Actions or Azure DevOps

If what you need is people rather than a delivered project, the same engineers are available through our hire Python developers page.

How we work

Python projects fail in two predictable ways: a prototype that was never meant for production is pushed into it, or a data pipeline grows by accretion until nobody can change it. Our process is built to avoid both.

1. Assessment. For a new build, the use case, the data sources, the volumes and who owns the result. For existing scripts and notebooks, a read of the code, the schedules, the credentials and the failure history, written up as risks and quick fixes.
2. Architecture note. A short document: services and jobs, where data lives, how secrets are handled, how the pipeline is monitored, what runs where in the cloud.
3. Walking skeleton. One end-to-end slice with tests, type checks, a container and a deployment, before the features arrive.
4. Increments. Two-week deliveries with a demo, reviewed pull requests, and for AI work an evaluation set that is run on every change so quality is measured rather than felt.
5. Hardening and handover. Load and failure testing where it matters, a runbook, documentation, then handover to your team or a maintenance agreement under defined SLAs.

Team and stack choices

A lead engineer in the United States owns the design and your weekly conversation; Python engineers at our India delivery center add capacity with overlapping hours; a QA engineer from our testing center of excellence tests each increment. New code is written in a current Python release with type hints, pytest and a linter from the first commit. We default to PostgreSQL, containers and a managed cloud runtime, and to Django or FastAPI depending on whether the application needs an admin and ORM or a lean typed API.

Send us the script or the idea and we will come back with a fixed-scope quote.

Where this fits with the rest of your systems

In most of the organizations we work with, Python is not the system of record. It is the layer that reads from the systems of record, transforms, predicts, summarizes and writes back. That is a good division of labor, and we design for it rather than fighting it.

PS3G’s core engineering practice is .NET and the Microsoft stack, which shapes how we pair Python with it. The usual pattern is a .NET or Java application that owns transactions and users, with Python services beside it for data pipelines and AI features, talking over an authenticated API or a message queue. A .NET web application can call a FastAPI endpoint that serves a model; a Python job can publish results that a .NET worker consumes; both can share one identity provider and one observability stack. We avoid letting the Python side reach into the application database directly, because that is how pipelines become fragile.

For Python web development that stands alone, Django is a complete application platform and we use it that way, with React in front when the interface needs it. For cloud, Python runs well on Azure Functions, AWS Lambda and Google Cloud Run, and the orchestration tools we use exist as managed services on all three.

Examples by organization size

These are typical shapes of engagement, not named clients.

• A founder with a product idea. A fixed-scope Django or FastAPI backend with authentication, a data model, an admin and a deployment pipeline, built so that the first in-house hire inherits tests and documentation rather than a notebook.
• A growing company drowning in spreadsheets. A dedicated engineer for a few months who turns the monthly reporting ritual into a scheduled pipeline into a warehouse, adds data-quality checks and alerts, and leaves the finance and operations leads with a dashboard they trust.
• A data group inside a larger organization. A model built by an internal data scientist is packaged, tested, served behind a typed API with logging and drift monitoring, and deployed inside the organization’s cloud and security standards. Ownership stays with the internal team; we provide the production engineering.

Engagement models

• Fixed-scope project: a defined deliverable and price for a backend, a pipeline or an AI feature.
• Dedicated team or staff augmentation: Python engineers inside your team on a monthly basis, scaled as needed.
• Managed services: we run, monitor and improve your Python services and pipelines under defined SLAs.
• Advisory: a code and pipeline review, an architecture for an AI feature, or fractional technical leadership.

How we start: a first conversation, a short assessment, a written proposal.

Why PS3G for Python work

We have built software for more than a decade from Wilmington, Delaware and our India delivery center, with Microsoft-certified and open-source-certified engineers on the same teams. We build and run our own products, so our Python work is shaped by what it takes to operate data and AI features in production, not just to demo them. Our AI and governance practices sit next door, which matters when a Python service handles customer data or produces output a regulator may ask about.

Related services

• All services: everything we build, test, run and advise on.
• Hire Python developers: engineers inside your team on a monthly basis.
• AI development: the use cases, models and governance behind Python AI integrations.
• Big data solutions: the platforms that Python pipelines feed.

Frequently asked questions

What do your Python development services include?

Web backends and APIs in Django and FastAPI, data pipelines and warehouse loading, automation scripts and services, and the integration of machine-learning models and large language models into applications. Each comes with tests, a container, a deployment pipeline and documentation, and is available as a fixed-scope project, staff augmentation, a managed service or an assessment.

Should I use Django or FastAPI?

Django when the application needs an admin, an ORM, authentication, forms and a conventional structure that a growing team can follow; it is a complete platform. FastAPI when you need a lean, typed API or a service that fronts a model or a queue, and the rest of the application lives elsewhere. Many systems use both, and we will recommend one during the assessment.

Can you turn our existing scripts and notebooks into something reliable?

Yes, and it is one of our most common requests. We read the scripts, schedules and credentials, write down the risks, then rebuild the pieces as tested, typed, scheduled jobs with logging, alerts and safe re-runs. The logic your team trusts is preserved; the fragility around it is removed. The work usually proceeds without stopping the current reports.

Is Python a good choice for AI integration?

It is the most common one. The model libraries, the LLM SDKs, the retrieval tooling and the evaluation frameworks are Python-first, so a Python service is the natural place for retrieval, prompting, tool calling and evaluation. The application that uses the result can stay in .NET, Java or PHP and call the Python service over an authenticated API.

Can Python work alongside our .NET systems?

Yes, and that is the pattern we recommend for most Microsoft-centered organizations: .NET keeps the transactional application and users, Python handles data pipelines and AI features, and the two talk over an API or a queue with one identity provider. PS3G builds both sides, so the contract between them is designed rather than negotiated after the fact.