Hire in days, not months

Hire Python Developers

Build reliable Python backends, automations, and AI workflows at $25/hour.

Get Python developers who can own Django and FastAPI services, automation scripts, data pipelines, AI integrations, and production backend reliability.

Get Python Profiles

Trust and delivery controls

Python delivery governance for backend and automation work

Protect production systems with explicit ownership, test expectations, error handling, and secure access patterns.

Controls before kickoff

Scope, access, ownership, and communication expectations are clarified before work starts.

Shortlist target

3-5 business days

Work focus

APIs + automation

Rate anchor

$25/hour

Vetted for practical delivery

Python Developer candidates are checked against real implementation, communication, and ownership scenarios.

Vetted

NDA and IP protection

Engagements include confidentiality terms, source-code ownership clarity, and least-privilege access.

Protected

Quality gates before release

Pull requests, tests, and release notes are reviewed against the risk profile of the work.

Controlled

Timezone-aware collaboration

Overlap windows, async updates, and written handoffs keep distributed teams aligned.

Aligned

Replacement path if fit changes

If the engagement shape changes, transition planning protects sprint continuity.

Continuity

Talent pool preview

Vetted Python Developer profiles ready to interview

Review a balanced shortlist with specialist, senior, and principal depth so you can hire for immediate delivery and long-term technical leadership.

View more profiles

Usman N.

Senior Python Developer

Vetted

8 years

Role-matched

PythonFastAPIPostgreSQLAWS

Architected a high-performance microservices backend for a SaaS platform using FastAPI, improving API response times by 45% and handling 5M+ daily requests.

Tariq C.

Python AI Engineer

Vetted

6 years

Role-matched

PythonOpenAILangChainPytest

Developed and deployed AI-powered features for a content platform, implementing RAG-style search and automated content summarization using LangChain.

Hussain R.

Principal Python Developer

VettedArchitect

12 years

Role-matched

DjangoSystem DesignDockerCI/CD

Led the backend transformation of a large enterprise application to a modern Django-based architecture, improving developer velocity by 40% and reducing technical debt.

Need a wider shortlist?

We can share additional python developer profiles by seniority, timezone, and domain fit.

Engagement models

Choose the model that matches your delivery pressure

Start lean for focused support or scale into a pod when roadmap pressure, QA, and coordination needs increase.

Pricing anchor

All priority hire engagements are scoped around $25/hour delivery with transparent capacity planning.

Part-time Python support

Best for focused backlog support, audit work, and steady delivery without full-time commitment.

Starts from $2,000 / month at $25/hour

Best for: Automation, API fixes, and backlog work

20-25 hrs/week
Weekly planning and reporting
Scoped sprint support

Dedicated QA, infrastructure, and third-party tools are scoped separately.

Get Started

Full-time Python developer

Recommended

Best for active roadmap work where one specialist owns delivery across consecutive sprints.

Starts from $4,000 / month at $25/hour

Best for: Backend roadmap ownership

40 hrs/week
Sprint-level ownership
Daily delivery updates

Platform fees, paid tools, and specialized audits are billed separately.

Get Started

Python delivery pod

Best for launch windows, parallel workstreams, and teams that need coordination plus execution.

Starts from $12,000 / month with $25/hour engineering rates

Best for: AI/data initiatives needing backend, QA, and PM support

Developer + QA + PM coordination
Parallel sprint execution
Release orchestration support

Compliance reviews and security audits are scoped separately when required.

Get Started

Hiring process

From Python scope to production-ready contribution

The process validates backend design, automation reliability, AI judgment, and data-handling expectations.

Typical kickoff window

Most teams can start with selected talent in 7-14 days.

The process reduces risk by validating role fit, ownership level, communication style, and delivery context before kickoff.

1
Backend Strategy Mapping
Step 1
We align on your specific stack needs, AI goals, and infrastructure requirements.
Day 1-2
2
Expert Shortlisting
Step 2
Review pre-vetted candidates with proven experience in Python and your chosen framework/cloud stack.
Day 2-5
3
Technical Deep Dive
Step 3
Interviews focused on Python architecture, API design, and real-world scaling challenges.
Day 5-10
4
Sprint Integration
Step 4
Smooth onboarding into your backend workflows and immediate development goals.
Day 7-14

Why teams hire through Codexty

Python talent selected for practical systems work

You get engineers who can turn backend, automation, and AI ideas into maintainable production workflows.

Best-rate positioning with quality controls

Competitive $25/hour delivery paired with vetting, governance, and release discipline.

Typical kickoff

7-14 days

Delivery focus

APIs + data + AI

Rate

$25/hour

Fast ramp for python developer work

Specialists join with clear scope, tool context, and first-sprint priorities.

Speed

Delivery standards before velocity

Speed is paired with code review, testing discipline, and documented tradeoffs.

Quality

Role-specific evaluation

Shortlists are matched to the stack, domain, and ownership level your roadmap requires.

Fit

Works inside your process

Talent adapts to your sprint cadence, ticketing, code review, and release workflow.

Integration

Built for shipping, not resumes

The goal is production contribution, not a long candidate screening loop.

Execution

Security-aware onboarding

Access, environments, and sensitive workflows are handled with practical guardrails.

Secure

Role-specific delivery scope

Python delivery lanes for APIs, automation, data, and AI-enabled products

Use Python specialists to build practical backend systems, internal automation, and AI workflows with maintainable production standards.

Backend APIs and platforms

Django and FastAPI services

Build secure APIs, admin tools, background jobs, and internal services for SaaS and operations teams.

Integration-heavy backend work

Connect payment, CRM, ERP, analytics, and third-party APIs with retry logic and clear error handling.

Production reliability improvements

Improve logging, monitoring, test coverage, and failure handling for Python services already in production.

Automation and data workflows

Internal workflow automation

Replace manual reporting, file processing, reconciliation, and support workflows with reliable Python automation.

Data pipeline implementation

Build extraction, transformation, validation, and scheduling workflows for analytics and operational data.

Fintech and regulated workflows

Support audit-friendly processing, validation, and access controls for sensitive operational systems.

AI and ML integrations

AI-assisted product features

Implement summarization, classification, recommendations, or document workflows with reviewable AI outputs.

ML service integration

Wrap model inference, batch scoring, and vector search into stable APIs that product teams can use.

Cost-aware AI operations

Track token usage, retries, caching, and fallback behavior so AI features stay predictable in production.

Production stack

Python stack choices for APIs, automation, data, and AI reliability

Stack planning covers frameworks, data access, background jobs, testing, observability, and AI service controls.

Python 3.x

Django

FastAPI

Flask

Pandas

NumPy

Scikit-learn

OpenAI / LangChain

PostgreSQL

Redis

MongoDB

SQLAlchemy

Docker

AWS

Kubernetes

GitHub Actions

Pytest

Sentry

Datadog

Black / Flake8

Hiring readiness

Python hiring playbook for backend, automation, and AI integration

Evaluate Python talent by API design, operational reliability, data judgment, and AI implementation discipline.

Role scope Interview playbook Business impact

Responsibilities / Role Scope

Owns

Backend feature implementation with high-quality, maintainable Python code
API design, documentation, and integration strategy
Data processing logic and AI/ML model integration
Automated test coverage and CI/CD pipeline integration

Collaborates on

Frontend engineers to define and implement efficient API contracts
Data scientists to deploy and scale ML models in production
Product managers to refine technical requirements and roadmap
DevOps to ensure smooth deployment and infrastructure reliability

Interview Questions

Structured by level for consistent and faster interviewer calibration.

junior

Fundamentals and execution reliability

What are the main differences between a list and a tuple in Python?
How do you handle exceptions in Python using try/except blocks?
What is a virtual environment and why is it important in Python development?
How do you perform a basic API request using the `requests` library?

mid

Delivery ownership and decision quality

Explain the difference between Django's ORM and SQLAlchemy.
How do you implement asynchronous tasks in Python using Celery or asyncio?
What are Python decorators and provide a common use case.
How do you optimize database queries and indexing in a Django or FastAPI app?
How do you handle secure authentication and authorization in a Python API?

senior

Architecture, risk control, and leadership

How do you design a scalable microservices architecture using Python and FastAPI?
How do you implement and scale AI/ML models in a production environment?
What strategies do you use for monitoring and debugging Python applications at scale?
How do you handle complex data processing and ETL pipelines with Python?
How do you ensure security best practices and compliance in a Python-based backend?

Why Outsource This Role

Faster qualified kickoff

Move from role brief to sprint-ready contribution without running a full recruiting cycle.

Typical kickoff: 7-14 days

Cost control at $25/hour

Add Python backend and automation capacity at $25/hour without hiring a full-time specialist before scope is proven.

Transparent rate: $25/hour

Lower delivery risk

Use scoped responsibilities, review gates, and release-ready handoffs to reduce rework.

Planning target: fewer backend defects over 2-3 sprints

AI-assisted delivery when useful

Use AI to speed boilerplate, test scenarios, data mapping, and workflow documentation while keeping backend logic reviewed.

Applied only where it improves speed, coverage, or cost

Flexible scale path

Start with one specialist and expand to a small pod when roadmap pressure increases.

Scale-up planning window: 2-3 weeks

Benefit	Outcome	Metric
Faster qualified kickoff	Move from role brief to sprint-ready contribution without running a full recruiting cycle.	Typical kickoff: 7-14 days
Cost control at $25/hour	Add Python backend and automation capacity at $25/hour without hiring a full-time specialist before scope is proven.	Transparent rate: $25/hour
Lower delivery risk	Use scoped responsibilities, review gates, and release-ready handoffs to reduce rework.	Planning target: fewer backend defects over 2-3 sprints
AI-assisted delivery when useful	Use AI to speed boilerplate, test scenarios, data mapping, and workflow documentation while keeping backend logic reviewed.	Applied only where it improves speed, coverage, or cost
Flexible scale path	Start with one specialist and expand to a small pod when roadmap pressure increases.	Scale-up planning window: 2-3 weeks

Client stories

Trusted by teams that ship fast

Real feedback from partnerships where we embedded with product teams, accelerated delivery, and stayed accountable to outcomes.

“What stood out was how quickly they understood both our codebase and business constraints. Their developer contributed meaningful pull requests in week one, improved our testing discipline, and proactively flagged architecture risks before they became expensive problems. It felt less like hiring a contractor and more like adding a senior teammate.”

Elena M.

VP Engineering, Fintech Platform

“Before working with Codexty, feature delivery took us 10 to 12 weeks because we were bottlenecked on implementation. After onboarding their engineer, we cut that cycle nearly in half and launched our new customer workflow in 6 weeks. The quality bar stayed high, and post-release bug volume was lower than our previous two launches.”

Mark S.

Product Lead, E-commerce Brand

“Our biggest concern was scalability during a period of rapid growth, and their team handled it with confidence. They refactored key backend services, introduced safer deployment practices, and helped us scale traffic without downtime during peak usage windows. We saw immediate performance gains and far fewer late-night incidents.”

Sarah K.

Engineering Manager, Enterprise Platform

FAQ

Answers to practical decision questions before you hire.

Can Python developers work on both Django and FastAPI?

Yes. We can match Python developers with Django, FastAPI, Flask, async APIs, background jobs, and data workflow experience.

Can Python developers build AI features?

Yes. They can integrate LLM APIs, vector search, classification, document workflows, and reviewable AI outputs when the use case is appropriate.

How quickly can we hire Python developer talent?

Most teams can review a shortlist within a few business days and begin onboarding in 7-14 days after role alignment.

What is the hourly rate for Python developer talent?

Priority hire pages use a transparent $25/hour rate, with part-time, full-time, and pod options scoped around your delivery needs.

How do you vet candidates before we interview them?

We validate practical delivery experience, communication quality, ownership level, and stack fit before presenting candidates.

Can the engineer work inside our existing tools and process?

Yes. Talent can work in your ticketing, repository, CI/CD, documentation, and communication workflows with clear onboarding guardrails.

Do you use AI during delivery?

AI-assisted delivery is used only when it aligns with customer requirements, improves speed or coverage, and remains under human review.

What if the first match is not the right fit?

We define a replacement path early so fit issues can be handled without losing sprint continuity or project context.