How fast can we add Python developers to our team?

You typically interview vetted candidates in 2 to 3 days, and most engineers are contributing within their first week. They work your business hours from day one.

How do you screen Python developers?

Against production signals, not trivia. The scorecard on this page is the real one: typed code, pytest discipline, deployment stories, and framework depth. We test how engineers ship, not what they memorize.

Can your engineers take an AI prototype to production?

Yes, and it is the most common Python engagement we run: RAG pipelines, LLM integrations, and model serving with real monitoring. We built systems like this for Meta and Discovery Channel; the case studies are on this page.

Django or FastAPI for our backend?

Django when you want batteries included and an admin out of the box. FastAPI for async APIs and typed contracts. We staff engineers deep in the framework you actually run rather than generalists across both.

What engagement models are available?

Staff augmentation, a dedicated team, or full project delivery. Most Python engagements start with one or two embedded engineers and grow from there.

Why hire through a development company instead of a freelance marketplace?

Continuity and accountability. Every engineer arrives vetted and stays backed by our team, so a departure or a scope change is our problem to absorb, not yours.

Which Python AI stacks do you work with?

OpenAI, Anthropic Claude, Google Gemini, LLaMA, and open models, with PyTorch and TensorFlow underneath. We have shipped our own AI products on these stacks every year since 2017, so the recommendations come from production use, not vendor decks.

Can you take over a Python system another vendor built?

Yes. Takeovers start with a code and infrastructure review, then a stabilization plan before new feature work. You get an honest read on what to keep, what to fix, and what to retire.

Hire Python Developer

Ship AI, data, and web projects with senior Python developers

Python is where our AI and data work lives. Azumo engineers used it to build enterprise search for Meta, voice apps for Discovery, and compliance platforms processing millions of records. Add that experience to your team in days.

When to Hire

When Teams Bring in Python Developers

Anyone can find a Python developer. Finding one who has shipped production systems is the hard part. Resumes flood in; most describe notebooks and tutorials, not software that ran under load.

We do that screening for you. Our engineers work your business hours and join as an extension of your team. AI coding assistants are part of their daily practice, accelerating the routine so senior time goes to data design and review.

You are hiring a company here, not browsing profiles. Every engineer arrives vetted and backed by our team.

AI feature going to production

A prototype that has to become a product, often RAG or an LLM integration.

Pipeline outgrew its cron jobs

Data work that needs real orchestration, testing, and monitoring.

Backend needs senior hands

A Django or FastAPI codebase past what generalists can carry.

Automation became critical

Internal tooling the business now depends on, quietly.

Skills and Use Cases

The Skills Your Python Project Requires

Python is a versatile, high-level programming language known for its simplicity, readability, and extensive standard library, widely used for web development, data analysis, machine learning, and more.

Our Python Developers always have

Django, FastAPI, and Flask in production

Data work with Pandas, NumPy, and PySpark

LLM integrations, RAG pipelines, and model serving

Typed, tested code: pytest, mypy, CI discipline

Async services with asyncio and task queues like Celery

Where Teams Use Python

AI features moving from prototype to production, including RAG and LLM integrations

Data pipelines and analytics platforms

Django and FastAPI backends for web and mobile products

Automation and internal tooling that became business-critical

Related Technologies:

Django

Flask

TensorFlow

Jupyter

Add a Python Developer

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How We Hire

How We Vet Python Developers

Python hides skill differences well. This is the scorecard we use to find engineers who have shipped production systems.

Production habits

Typed code, pytest, dependency discipline with Poetry or uv, environments that reproduce

Ships from a notebook and hopes

Performance

Knows when to vectorize, when to use multiprocessing, and when Python is the wrong tool

Adds workers before ever profiling

Framework depth

Django, FastAPI, or Flask in production, deep in one lane

Passing familiarity with all three, depth in none

ML judgment

Model serving, data validation, and drift monitoring

Calls an API and calls it ML engineering

Async

Knows where asyncio pays off and where it bites

Sprinkles async on synchronous code

Our favorite filter: What broke the last time you deployed a Python service, and how did you find it?

Our Experience

Python Work We Have Shipped

Python runs through most of our client work. For Meta we built generative AI enterprise search across 3.5 million supplier records. For Discovery Channel we shipped a multilingual voice app backed by PyTorch, TensorFlow, and spaCy. For Six Lambda we built a compliance data platform on Django, Airflow, and Elasticsearch.

Our own products run on Python too, including the Charli voice agent on this page. The case studies below are the engagements where Python carried the load.

Used in the Case Studies

Angle Health

AI-Powered Talent Intelligence Company

Major Midstream Oil and Gas Company

Centegix

Charlibot

Sparks & Honey

Case studies from our Python engagements

Stovell AI

Fintech AI Development: Predictive Analytics for Alpha Generation

Read the Case Study

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Why Azumo for Your Software Development

Ship faster with engineers who build with and for AI. We have delivered production ready solutions since 2016.

JP Lorandi, Azumo's CTO wearing a black collared shirt against a white background.

"Our engineers build production AI every day for our clients and our own primitives. That's the difference between a team that's used AI and one that ships it.”

Juan Pablo Lorandi
CTO, Azumo · 25+ years of software architecture experience.
Certified Claude Architect

Build With AI

Engineers develop with AI daily, compressing delivery cycles without cutting corners.

Senior by Default

We hire for seniority and test for it before anyone joins your team.

Scale on Demand

Grow or shrink the team as your roadmap changes — no renegotiation drama.

Time-Zone Aligned

Real-time collaboration across your full working day, from Latin America.

Engagement That Fits

Dedicated team, staff augmentation, or full project build. You pick the model.

Frequently Asked Questions

Q:
How fast can we add Python developers to our team?
You typically interview vetted candidates in 2 to 3 days, and most engineers are contributing within their first week. They work your business hours from day one.
Q:
How do you screen Python developers?
Against production signals, not trivia. The scorecard on this page is the real one: typed code, pytest discipline, deployment stories, and framework depth. We test how engineers ship, not what they memorize.
Q:
Can your engineers take an AI prototype to production?
Yes, and it is the most common Python engagement we run: RAG pipelines, LLM integrations, and model serving with real monitoring. We built systems like this for Meta and Discovery Channel; the case studies are on this page.
Q:
Django or FastAPI for our backend?
Django when you want batteries included and an admin out of the box. FastAPI for async APIs and typed contracts. We staff engineers deep in the framework you actually run rather than generalists across both.
Q:
What engagement models are available?
Staff augmentation, a dedicated team, or full project delivery. Most Python engagements start with one or two embedded engineers and grow from there.
Q:
Why hire through a development company instead of a freelance marketplace?
Continuity and accountability. Every engineer arrives vetted and stays backed by our team, so a departure or a scope change is our problem to absorb, not yours.
Q:
Which Python AI stacks do you work with?
OpenAI, Anthropic Claude, Google Gemini, LLaMA, and open models, with PyTorch and TensorFlow underneath. We have shipped our own AI products on these stacks every year since 2017, so the recommendations come from production use, not vendor decks.
Q:
Can you take over a Python system another vendor built?
Yes. Takeovers start with a code and infrastructure review, then a stabilization plan before new feature work. You get an honest read on what to keep, what to fix, and what to retire.