Vetted and shortlisted in 72 hours - not just matched in 24. Get 3 Python engineers screened on Django, FastAPI, Flask, or data pipelines via live coding, not a CV filter.
Trusted by fast-moving teams
Python is the most over-claimed language on freelance platforms. We fix that with live coding screens, framework-specific matching, and a single transparent fee.
Python is the #1 language on Upwork by volume - and the most over-claimed. Our AI filters 400+ vetted engineers by framework, seniority, and timezone. You get 3 matched candidates, not 80 proposals to sift through.
Half the Python CVs on freelance platforms claim Django and FastAPI experience that evaporates in a real interview. Every candidate passes a live screen: async patterns, ORM design, API architecture, and a framework-specific challenge. No exceptions.
Upwork's combined client and freelancer fees add up to 10-30% - paid by both sides. We charge one flat 15% on the developer rate. No bidding chaos. No hidden costs. No surprises at invoice time.
A Django engineer is not a FastAPI engineer. We match on your actual architecture - Django ORM and DRF for monoliths, FastAPI async for AI/ML service backends, Flask blueprints for microservices. Wrong framework = weeks of ramp-up.
No long-term contracts. No exit penalties. Engagements run on 14-day notice. You own 100% of all code, models, data pipelines, and scripts through a tri-party contract signed on day one.
Data pipeline and backend API projects fail when accountability is absent. Weekly delivery reports, milestone tracking, and dispute resolution are standard on every engagement - not a premium add-on.
Anonymised profiles - real ships, real output. Every engineer passed a live Python coding screen. Your shortlist is matched to your exact framework and project type.
Priya M.
Senior Django + DRF Engineer
Alex K.
FastAPI + Async Python Engineer
Fatima O.
Python Data Engineer
Daniel L.
Flask + Microservices Architect
Nikhil R.
Python Automation + Scripting Engineer
Sofia K.
Django Full-Stack + ML Integration
These are sample profiles. Your shortlist is built to match your exact Python framework, seniority, and project type.
$0 matching fee · 72 hrs · 14-day guarantee
No recruiter calls. No proposal inbox. Four steps and you have a live-coded, vetted Python engineer starting work.
Tell us your Python framework, seniority, deliverable, and timeline. Takes 5 minutes. Specify Django, FastAPI, Flask, data pipelines, or AI/ML backend - we match on the full stack, not just 'Python developer'.
Our AI scans 400+ vetted Python engineers and shortlists 3 who match your framework, seniority, and timezone. Python is the largest pool on Upwork - the AI filter is genuine value here.
Meet your 3 shortlisted engineers. Every candidate has passed a live screen covering Python patterns, ORM design, async programming, and a framework-specific challenge. You choose who to hire.
Tri-party contract signed on day one. Weekly delivery reports, milestone tracking, and dispute resolution built in. Especially important for data pipeline and backend API projects. You own 100% of all code and IP. Delivery includes pinned dependencies in requirements.txt or pyproject.toml with a reproducible virtual environment setup, a test coverage report at ≥80% line coverage, and docstring coverage following Google or NumPy style conventions. For Django projects, migration history is reviewed before handover to confirm no squashed or missing migrations.
No commitment · 3 vetted engineers shortlisted in 72 hrs
Python is the most versatile backend language - web APIs, data pipelines, AI/ML services, automation, and fintech backends. Here is what our engineers deliver.
Shortlisted in 72 hrs · $0 matching fee
The most common Python hiring mistake is matching the wrong framework to the project. Here is how to choose - and how we match engineers to your exact stack.
Batteries-included for monoliths & platforms
Async-first for APIs, AI services & microservices
Minimal & flexible for microservices
Pydantic v2
Data validation + settings management
SQLAlchemy 2
Async + sync ORM for all databases
Celery + Redis
Async jobs, scheduling, rate limiting
Pandas 2
DataFrame operations, ETL, analytics
NumPy
Numerical computing, arrays, linear algebra
httpx / aiohttp
Async HTTP requests and integrations
pytest
Unit, integration and API testing
Alembic
Database schema migrations with SQLAlchemy
Poetry / uv
Dependency management and virtual envs
Python 3.12+
Type hints, match statements, perf improvements
Know exactly what you are hiring. This matrix maps what each seniority level can realistically own so you get the right fit for your project scope.
Python 3.10+, basic OOP, comprehensions, standard library, pip, venv
Decorators, generators, context managers, type hints, async/await basics, dataclasses
Python internals, GIL, descriptors, metaclasses, full async patterns, performance profiling
Django basics - models, views, URLs, templates, Django Admin setup
DRF serialisers and viewsets, FastAPI routing + Pydantic models, Flask blueprints
Framework internals, custom middleware, multi-database setups, plugin/extension architecture
Django ORM CRUD, basic SQL, PostgreSQL setup, simple migrations with Alembic
Complex ORM queries, query optimisation, indexes, SQLAlchemy async, raw SQL when needed
Query plan analysis, connection pooling (PgBouncer), read replicas, sharding strategies, NoSQL
REST API consumption with requests/httpx, basic JSON serialisation, simple OAuth flows
Designing RESTful APIs, GraphQL with Strawberry, Pydantic validation, rate limiting, API versioning
API gateway patterns, gRPC with Python, event-driven architecture, complex auth (SAML, OIDC)
Celery tasks for basic background jobs, understanding threading vs multiprocessing
asyncio event loop, async SQLAlchemy, Celery Beat scheduling, Redis pub/sub
High-concurrency FastAPI services, asyncio task management, distributed task queues (Dramatiq)
pytest basics, simple unit tests, fixtures, coverage reports
Mocking with unittest.mock, integration tests, factory_boy, test database setup, TDD basics
Test strategy, property-based testing with Hypothesis, mutation testing, performance benchmarks
Pandas basics, CSV/JSON parsing, simple scripts for data transformation
Pandas optimisation, Airflow DAG authoring, dbt models, SQLAlchemy ETL patterns
Distributed data processing (Spark, Dask), Kafka consumers, real-time pipeline architecture
Docker basics, environment variables, requirements.txt, basic CI pipelines
Docker Compose, Gunicorn/Uvicorn configuration, AWS Lambda deployment, GitHub Actions CI/CD
Kubernetes deployment of Python services, Helm charts, Blue/Green deploys, observability setup
Python 3.10+, basic OOP, comprehensions, standard library, pip, venv
Decorators, generators, context managers, type hints, async/await basics, dataclasses
Python internals, GIL, descriptors, metaclasses, full async patterns, performance profiling
Django basics - models, views, URLs, templates, Django Admin setup
DRF serialisers and viewsets, FastAPI routing + Pydantic models, Flask blueprints
Framework internals, custom middleware, multi-database setups, plugin/extension architecture
Django ORM CRUD, basic SQL, PostgreSQL setup, simple migrations with Alembic
Complex ORM queries, query optimisation, indexes, SQLAlchemy async, raw SQL when needed
Query plan analysis, connection pooling (PgBouncer), read replicas, sharding strategies, NoSQL
REST API consumption with requests/httpx, basic JSON serialisation, simple OAuth flows
Designing RESTful APIs, GraphQL with Strawberry, Pydantic validation, rate limiting, API versioning
API gateway patterns, gRPC with Python, event-driven architecture, complex auth (SAML, OIDC)
Celery tasks for basic background jobs, understanding threading vs multiprocessing
asyncio event loop, async SQLAlchemy, Celery Beat scheduling, Redis pub/sub
High-concurrency FastAPI services, asyncio task management, distributed task queues (Dramatiq)
pytest basics, simple unit tests, fixtures, coverage reports
Mocking with unittest.mock, integration tests, factory_boy, test database setup, TDD basics
Test strategy, property-based testing with Hypothesis, mutation testing, performance benchmarks
Pandas basics, CSV/JSON parsing, simple scripts for data transformation
Pandas optimisation, Airflow DAG authoring, dbt models, SQLAlchemy ETL patterns
Distributed data processing (Spark, Dask), Kafka consumers, real-time pipeline architecture
Docker basics, environment variables, requirements.txt, basic CI pipelines
Docker Compose, Gunicorn/Uvicorn configuration, AWS Lambda deployment, GitHub Actions CI/CD
Kubernetes deployment of Python services, Helm charts, Blue/Green deploys, observability setup
Seniority levels are guidelines. Rate and availability depend on your specific requirements. Tell us your project scope and we match the right level.
Python is the most over-claimed language on freelance platforms. Here is the interview playbook our senior engineers use - adapted so non-technical founders can use it too.
Explain the difference between @staticmethod, @classmethod, and instance methods in Python.
Why ask it: Reveals depth of OOP understanding. Candidates who only know @property and basic class syntax will struggle here.
Red flag: Cannot explain when to use classmethod for factory patterns vs staticmethod for utilities.
Walk me through how you would structure a Django project for a team of 5 engineers. What are your go-to reusable apps?
Why ask it: Tests architectural thinking, not just syntax. Strong candidates will mention separation of concerns, reusable apps, and settings management.
Red flag: Describes a single monolithic apps.py with all models - shows no experience with larger codebases.
What is the N+1 query problem in Django ORM? Show me how you would detect and fix it.
Why ask it: This is the most common cause of Django performance disasters. Every mid+ level engineer must understand select_related and prefetch_related.
Red flag: Has not heard of Django Debug Toolbar or django-silk - likely has never profiled their own code.
Explain Python's asyncio event loop. When would you choose async/await over threads or multiprocessing?
Why ask it: Critical for FastAPI engineers and anyone building I/O-bound services. Confusing I/O-bound vs CPU-bound workloads is a fundamental misunderstanding.
Red flag: Says 'async makes everything faster' - does not understand it only helps I/O-bound work.
How would you design a rate-limited API endpoint in FastAPI that allows 100 requests per user per minute?
Why ask it: Tests real-world API design knowledge - Redis, middleware, dependency injection. Shows whether they can build production-ready services.
Red flag: Proposes a global in-memory counter - will fail under multiple worker processes.
You have a Pandas DataFrame with 10 million rows and a transformation that takes 45 seconds. Walk me through how you would optimise it.
Why ask it: Reveals whether the candidate understands vectorisation, dtypes, chunking, and when to move beyond Pandas to Polars or Spark.
Red flag: Suggests adding more RAM rather than optimising the computation - indicates no real data engineering experience.
Cannot write a working Django model with a ForeignKey and Meta class from memory
Any genuine Django developer has written hundreds of models. This is table stakes.
Lists FastAPI on CV but cannot explain what Pydantic does or why it matters
FastAPI's entire value proposition is Pydantic validation. Not knowing it means they copy-pasted a tutorial.
Describes Python 2 patterns (print statement, xrange, unicode vs str) as current practice
Python 2 EOL was 2020. Recent Python 3.12+ features should be on their radar.
Has never used a migration tool - manually writes ALTER TABLE statements in production
Alembic and Django migrations are standard. Not using them creates untracked schema state.
Cannot explain the difference between a Celery worker and a Celery beat scheduler
Any backend Python dev who claims async task queue experience must know this.
CV lists 'Python, Django, Flask, FastAPI, Pandas, NumPy, TensorFlow, PyTorch, Airflow, dbt' - 10 frameworks with equal confidence
Genuine Python engineers are deep in 1-2 frameworks. This list signals a padded CV with no real depth.
Junior Django / Flask (0-2 yrs)
CRUD features, ticket work, supervised
Mid-Level Django / FastAPI (3-5 yrs)
Feature ownership, API design, some architecture
Senior Python Engineer (6+ yrs)
System design, mentorship, framework internals
Python Data Engineer (pipeline focus)
Airflow, dbt, Snowflake, data modelling
FastAPI / AI Backend Engineer
Async APIs, LLM integrations, model serving
Python Tech Lead / Architect
Full system ownership, team-of-5+ guidance
Rates in USD/hr. Open IT Freelancers adds a flat 15% management fee on the developer rate. $0 matching cost.
Transparent market benchmarks. Open IT Freelancers adds a flat 15% management fee on top of the developer rate - no hidden costs, no surprises.
Eastern Europe
Poland, Ukraine, Romania, Serbia
South Asia
India, Pakistan, Sri Lanka
South-East Asia
Philippines, Vietnam, Indonesia
Latin America
Brazil, Argentina, Colombia, Mexico
Middle East & Africa
Egypt, Nigeria, Kenya, UAE
Western Europe
UK, Germany, Netherlands, France
$0
Matching Fee
Post your brief and receive 3 shortlisted Python engineers at zero cost
+15%
Flat Management Fee
Applied on developer rate only. No per-milestone fees, no exit charges
14-day
Free Replacement
If the engineer is not the right fit in the first 14 days, we replace at no extra cost
All rates in USD. Benchmarks based on Open IT Freelancers network data and Stack Overflow Developer Survey 2025. Actual rate depends on seniority, framework specialisation, availability, timezone, and engagement length.
An honest comparison across every dimension that matters when hiring Python developers.
Time to first shortlist
72 hours
Instant (60-200 proposals)
1-2 weeks
4-12 weeks
Vetting - Python specific
Live coding screen: async, ORM design, framework-specific challenge
Self-reported only - Python is the most over-claimed language
Technical interview (adds 1-2 weeks)
Your own interview loop
Framework matching
Matched on Django / FastAPI / Flask / data stack / AI backends
You filter by keyword - 'Python developer' covers wildly different skill sets
Stack matching available
Depends on your JD and recruiter knowledge
Number of candidates to review
3 pre-vetted engineers
60-200 proposals in the inbox
1-3 (narrow pool)
30-80 applications
Platform fee model
Flat 15% on developer rate
5-10% client + 5-20% freelancer - combined 10-30%
40-60%+ opaque margin on developer rate
Salary + benefits + equipment + $5-25K recruiter fee
Managed delivery
Weekly reports, milestones, disputes - standard on all engagements
None - you manage directly
Account manager available at higher tiers
Full management overhead on you
Data pipeline project accountability
Milestone-based delivery with weekly pipeline health reports
None - pipelines break silently with no accountability
Basic account management
Internal manager required
IP and code ownership
100% yours - tri-party contract signed on day one
Requires your own contract
Contract included
Standard employment contract
14-day replacement guarantee
Yes - activated within 24 hours
No
No
No - re-hire from scratch
Contract flexibility
14-day notice, zero exit penalties
Project-by-project, hourly
Minimum engagement terms apply
Notice period, potential severance
Matching cost
$0
$0 (but hours reviewing proposals)
$0 (paid in higher opaque rates)
$5K-$25K recruiter fee
Upwork
$75/hr + ~$15/hr client fee + $7/hr freelancer fee passed through = ~$97/hr effective
~$46,560 over 12 weeks
Toptal
$75/hr developer cost + Toptal's ~50% margin = $112/hr billed to you
~$53,760 over 12 weeks
Open IT Freelancers
$75/hr + flat 15% management fee = $86.25/hr. That's all.
~$41,400 over 12 weeks - $5-12K less
Based on 40 hrs/wk × 12 weeks = 480 hrs. Upwork client fee estimate 20%; Toptal margin estimate 50%. Actual figures vary.
From a 2-person founding team shipping an MVP to a 500-person company rebuilding its data infrastructure - here is how Python hiring works at each stage.
Ship an MVP fast. Our Python engineers build Django or FastAPI backends, integrate third-party APIs, and hand over production-ready codebases - not prototypes.
You have product-market fit and your existing Python codebase is slowing you down. We match engineers who specialise in refactoring, performance, and scaling Django or FastAPI to handle 10× the load.
Compliance, governance, and accountability matter at this scale. We provide vetted Python engineers with enterprise experience - Django admin for ops, data pipeline architecture, and full managed delivery with weekly reporting.
You win the project, we staff the Python engineers. No IR35 headaches, no employment overhead - just vetted Python developers who show up, write good code, and fit into your team's workflow.
FastAPI · PostgreSQL · Stripe · Celery
Build payment processing API with KYC integration, webhook handling, and async transaction reconciliation. 12 weeks.
Airflow · dbt · Snowflake · Python 3.12
Migrate legacy cron-based ETL to Airflow DAGs feeding Snowflake. 6 source systems, 200GB/day. 16 weeks.
Django · DRF · Celery · Redis · PostgreSQL
Build multi-tenant SaaS backend - subscriptions, RBAC, bulk email via Celery, REST API for React frontend. 20 weeks.
Everything founders and engineering managers ask us before their first Python engagement.
Every engineer passes a live coding screen - not a take-home test or an automated quiz. The screen covers: Python idioms and advanced patterns, ORM design and query optimisation, async/await and event loop understanding, and a framework-specific challenge (Django ORM, FastAPI dependency injection, or Flask blueprints). We specifically test for real-world patterns, not syntax knowledge - and we screen out engineers who paste Stack Overflow answers without understanding the code. The live Python screen covers Django ORM N+1 query detection and select_related/prefetch_related correction, FastAPI dependency injection patterns and async context management, Python async generator patterns (async for, aiter), and typing coverage with mypy strict mode. Engineers are also tested on virtualenv isolation and pyproject.toml dependency pinning - production Python projects that cannot be reproduced cleanly are a red flag we surface early.
You receive 3 shortlisted, pre-vetted Python engineers within 72 business hours of submitting your brief. Our AI scans 400+ engineers and a senior Python engineer validates each match manually for framework fit (Django, FastAPI, Flask, data stack), seniority, and timezone compatibility.
Developer rates range from $20 to $160 per hour depending on seniority and region. Open IT Freelancers adds a flat 15% management fee on top of the developer rate. There is no matching fee - you pay $0 to receive your shortlist.
We match on your full framework and library stack. We have engineers specialising in Django (DRF, ORM, Celery, full-stack with templates), FastAPI (async APIs, Pydantic models, AI/ML service backends), Flask (microservices, blueprints, lightweight APIs), and Python data stack (Pandas, NumPy, SQLAlchemy, Airflow, dbt). Tell us your stack in the brief and we match accordingly.
Yes. Python is the primary language for AI/ML and data engineering. We have engineers skilled in building FastAPI backends for ML models, data pipelines with Airflow and dbt, Pandas and NumPy data processing, and integrating with LLM APIs (OpenAI, LangChain, Anthropic). For dedicated ML model training engineers, see our Hire AI & ML Developers page.
Django developers are strongest building full-featured monolithic web applications with admin panels, complex data models, and DRF APIs. FastAPI developers excel at high-performance async APIs and AI/ML service backends. Flask developers work best on lightweight microservices and single-purpose APIs. The worst Python hiring mistake is treating these as interchangeable - we match on your specific architecture to avoid expensive mismatches.
Yes. Engagements can be as short as 2-4 weeks (for a focused script, API integration, or audit) or as long as 24+ months. The only commitment is a 14-day notice period to end an engagement, with no early termination penalties.
If within the first 14 days the engineer is not the right fit, we replace them at no extra cost. No arguments, no delays - we activate the replacement process within 24 hours. After 14 days, engagements run on 14-day notice.
You own 100% of all code, scripts, data models, pipelines, and documentation. Ownership transfers automatically on work approval through our tri-party contract signed on day one. There are no platform licensing fees or IP retained by the engineer or Open IT Freelancers.
We screen for codebase navigation and legacy code capability, not just greenfield skills. In the brief, tell us your existing setup - Django version, ORM patterns, Celery config, etc. - and we match engineers who have worked on similar setups and can ramp up quickly without forcing a full rewrite.
Toptal's vetting process takes 1-2 weeks and their margin on developer rates is typically 40-60%. We return a shortlist in 72 hours with a flat 15% fee. Our live Python screen is specific to real-world patterns - ORM design, async patterns, framework internals - not generic algorithm tests. We also include managed delivery (weekly reports, milestone tracking) as standard, which Toptal charges extra for.
Timezone is part of our matching criteria. When you post your brief, specify your team's working hours and we will only shortlist engineers with sufficient timezone overlap - typically 4+ hours of shared working time. We have engineers across Eastern Europe, South Asia, Latin America, South-East Asia, and Western Europe.
Before You Hire
Written by our engineering team - no fluff, just the frameworks that work.
Developer rates by region, hidden cost breakdown, TCO comparison across DIY vs managed platforms, and a complete budgeting guide.
The 7-day vetting framework, technical interview questions, take-home test design, and red flags - built for non-technical founders.
Stage-by-stage decision framework for founders and CTOs. Cost, speed, and risk tradeoffs - with a clear verdict for each company type.
Post your brief in 5 minutes. We handle the live coding screens, framework matching, and managed delivery. You get engineers who actually know Python.
No commitment · 72 hrs · $0 to start