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Dedicated Python developers for Django and FastAPI backends, async services, data pipelines and LLM integrations, writing typed, tested code that fits your repositories, CI and code review process.

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A cleaner way to hire Python developers

Hire Python developers from Krapton when you need someone who writes production Python, not notebooks that happen to run on a server. Python covers more ground than most languages: Django and FastAPI backends, Celery workers, data...

Shortlist
24h
Replacement
30d
IP yours
100%

  • Framework choice with reasons

    Django suits admin-heavy products with relational data and built-in auth; FastAPI suits typed APIs and async I/O; Flask still fits small services. Your developer explains which one matches your product instead of defaulting to whatever they used last.

  • Reproducible environments

    Dependency drift breaks Python projects quietly. Developers declare dependencies in pyproject.toml, pin them with a uv or Poetry lockfile, build slim images with multi-stage Dockerfiles, and scan with pip-audit so a transitive package update cannot surprise production.

  • Async done correctly

    A single blocking database driver call inside an async FastAPI route stalls the whole event loop. Your developer uses async-native drivers such as asyncpg, offloads CPU work to process pools or task queues, and load-tests with Locust to prove concurrency holds.

  • Data and AI fluency

    Python is where your data and model work already lives. A developer comfortable with pandas, Polars, DuckDB, scikit-learn and the OpenAI or Anthropic SDKs can connect product features to that work without handing every task to a separate data team.

Why Python?

What makes Python the right choice for modern engineering teams.

  • Versatile and interpreted language

    Python is a versatile, high-level programming language with an easy-to-read syntax, facilitating rapid development and maintenance.

  • Large standard library

    Python comes with a comprehensive standard library, providing modules and packages for various tasks, reducing the need for external dependencies.

  • Community and documentation

    Python boasts a large and active community, offering extensive documentation, support forums, and a wealth of resources for developers.

  • Cross-platform compatibility

    Python is cross-platform compatible, running on various operating systems such as Windows, macOS, and Linux, ensuring broad deployment options.

  • Dynamic typing

    Python is dynamically typed, allowing for flexibility in variable types and simplifying code development and readability.

What you can build with Python

  • Django SaaS backends

    Multi-tenant Django apps with the ORM, Django REST Framework or Django Ninja APIs, the admin for internal operations, Celery for background jobs and PostgreSQL as the system of record.

  • Typed FastAPI services

    Pydantic v2 models as the contract, automatic OpenAPI docs for frontend and partner teams, dependency injection for auth and database sessions, and SQLAlchemy 2.0 with Alembic migrations underneath.

  • LLM and RAG services

    Retrieval pipelines that chunk and embed documents, store vectors in pgvector or Qdrant, call model APIs with retries and timeouts, stream tokens to the client and log prompts for evaluation.

  • Data pipelines and ETL

    Scheduled ingestion with Airflow, Dagster or Prefect, transformations in Polars or dbt, validation with Pandera or Great Expectations, and loads into Postgres, BigQuery or Snowflake that are idempotent and safe to rerun.

  • Automation and integrations

    Scripts and workers that sync CRMs, ERPs and spreadsheets, scrape sites responsibly with Playwright or httpx, parse PDFs and email, and run on schedules with structured logging and alerting.

  • Internal tools and CLIs

    Command-line tools built with Typer or Click, Streamlit dashboards for operations teams, and packaged libraries published to a private index so other services reuse the same business logic.

Keeping Python codebases fast and maintainable

Python’s reputation for slowness is mostly about how it is used. Most web services spend their time waiting on databases and network calls, so the real wins come from fewer queries, connection pooling, caching and correct async usage rather than a language switch. Where CPU genuinely dominates, the fix is usually to push work into vectorized libraries such as NumPy and Polars, which run in compiled code, or to isolate the hot path in a Rust extension built with PyO3. The free-threaded CPython build, officially supported since Python 3.14, opens real multi-core threading, but C extensions must declare support and some still lag, so it is evaluated per workload rather than switched on everywhere. The developer measures first with py-spy flame graphs and request traces, then proposes the smallest change that moves the numbers.

Maintainability is the other half. Python lets a codebase run for years without types or tests, and that freedom becomes the cost later when nobody can refactor safely. Krapton developers add structure incrementally: type hints on new and touched code, a strict mypy profile that expands module by module, pytest coverage around money, permissions and data integrity first, and a clear layering between web handlers, domain logic and persistence so frameworks can be upgraded without touching business rules. They also keep the toolchain boring and current. uv replaces a tangle of pip, virtualenv and pip-tools commands, Ruff replaces several linters, and pre-commit hooks run the same checks locally that CI enforces. Deprecation warnings are treated as failures in CI, which turns the next framework upgrade into a routine pull request instead of a project.

Python in action

import asyncio
import aiohttp
import requests
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt

# Function for synchronous web scraping
def scrape_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    numbers = [int(s.text) for s in soup.find_all('span', class_='number')]
    return numbers

# Function for asynchronous web scraping
async def async_scrape_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            text = await response.text()
            soup = BeautifulSoup(text, 'html.parser')
            numbers = [int(s.text) for s in soup.find_all('span', class_='number')]
            return numbers

# Function for data processing
def process_data(data):
    df = pd.DataFrame(data, columns=['Value'])
    df['RollingAverage'] = df['Value'].rolling(window=5).mean()
    return df

# Function for data visualization
def plot_data(df):
    plt.plot(df.index, df['Value'], label='Value')
    plt.plot(df.index, df['RollingAverage'], label='Rolling Average', linestyle='--')
    plt.xlabel('Index')
    plt.ylabel('Values')
    plt.title('Data Visualization')
    plt.legend()
    plt.show()

# Async main function
async def main(urls):
    tasks = [asyncio.create_task(async_scrape_data(url)) for url in urls]
    results = await asyncio.gather(*tasks)
    return results

# URLs for demonstration (Replace with actual URLs)
urls = ["http://example.com/data1", "http://example.com/data2"]

# Run the async main function and process/visualize data
scraped_data = asyncio.run(main(urls))
for data in scraped_data:
    df = process_data(data)
    plot_data(df)

What our Python developers know

Every Krapton developer is vetted with real production experience in Python across multiple industry domains.

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  • Core PythonProficient in the fundamentals of Python, including variables, data types, functions, and control structures.
  • Standard library usageExpertise in utilizing the Python standard library for a wide range of tasks, minimizing reliance on external dependencies.
  • Community interactionActively engaged with the Python community, staying updated on best practices, new libraries, and contributing to discussions.
  • Cross-platform developmentExperience in developing Python applications that run seamlessly on different operating systems, ensuring cross-platform compatibility.
  • Object-oriented programming (OOP)Mastery of object-oriented programming principles in Python, designing modular and maintainable code structures.

What our Python developers go deep on

  • Typing and static analysis

    Type hints throughout, mypy or Pyright in strict mode for new modules, Pydantic models at service boundaries, and Ruff configured as the single linter and formatter so reviews focus on logic.

  • Testing with pytest

    Fixtures and factories with factory_boy, parametrized tests, Hypothesis for property-based edge cases, Testcontainers for real databases, and coverage gates in CI that block merges when critical paths lose tests.

  • Concurrency and performance

    Choosing between asyncio, threads and multiprocessing for the workload, profiling with py-spy and cProfile, moving hot loops to NumPy or Polars, and evaluating the free-threaded CPython build where the GIL is the bottleneck.

  • Background jobs and queues

    Celery with Redis or RabbitMQ, Dramatiq or RQ for simpler needs, Temporal for long-running workflows, idempotent task design, visibility timeouts and dead-letter handling so retries do not double-charge a customer.

  • Security for Python services

    Bandit and pip-audit in CI, secrets kept out of code with environment-based settings, safe deserialization (no pickle from untrusted input), parameterized SQL through the ORM, and Django’s security middleware configured properly.

  • Deployment and observability

    Gunicorn with Uvicorn workers or Granian behind a reverse proxy, container health checks, OpenTelemetry tracing, structured JSON logs with structlog, and Sentry for exceptions with release tracking.

Common Python projects we deliver

  • Stabilizing a Django monolith that grew fast

    The developer adds django-debug-toolbar and query logging to find N+1 patterns, introduces select_related and prefetch_related, moves slow work into Celery tasks, and raises test coverage around billing and permissions before refactoring anything risky.

  • Upgrading off an end-of-life Python version

    Python 3.9 is past end of life, and many older Django releases are too. Upgrades proceed stepwise: run the suite on the target interpreter in a CI matrix, update dependencies with uv, fix type and lint findings, then roll out behind a canary.

  • Adding an AI feature to an existing product

    A Python service sits beside your main app and owns prompts, retrieval and evaluation. The developer defines a typed API, adds caching and rate-limit handling for model calls, and builds a small evaluation set so prompt changes are measured, not guessed.

  • Replacing fragile cron scripts with orchestration

    Dozens of cron jobs with no retries become a Dagster or Prefect project: each job gets inputs and outputs declared, retries and alerts configured, logs centralized, and a backfill path for the days a source system was down.

Python solutions by industry

  • Fintech

    Reconciliation jobs, risk scoring and reporting services where Decimal arithmetic, audit logs and reproducible data pipelines matter more than raw speed.

  • Life sciences

    Clinical data processing, FHIR integrations and analysis code that must be validated, version-pinned and kept separate from identifiable patient data.

  • Logistics

    Route optimization with OR-Tools, demand forecasting, carrier API integrations and event-driven tracking services that process large volumes of status updates.

  • EdTech

    Learning platforms on Django with content management, assessment engines, analytics on learner progress, and AI tutoring features backed by retrieval over course material.

Python vs Go

Python and Go often compete for the same backend role. Python wins when the product touches data science, machine learning or LLM tooling, or when delivery speed and library coverage matter most. Go wins for high-concurrency network services, CLIs and infrastructure where predictable latency and small binaries pay off. Plenty of teams run both, with Python for data and AI services and Go for hot paths.

Python vs Go
DimensionPythonGo
Development speedVery fast to write; huge library ecosystemFast, but more boilerplate for data work
Raw throughputSlower per core; scale with processes or asyncCompiled, efficient goroutines, low memory per request
Concurrency modelasyncio and multiprocessing; free-threaded build maturingGoroutines and channels built into the language
AI and dataDefault language for ML, LLM tooling, notebooksLimited ML ecosystem; mostly calls external services
DeploymentInterpreter plus dependencies; containers recommendedSingle static binary, small container images
TypingOptional hints checked by mypy or PyrightStatic types enforced by the compiler

How to hire Python developers in 5 steps

A transparent, low-risk process — from first call to onboarded engineer in about two business days.

  1. 01Day 0

    Share your brief

    Tell us the stack, scope, seniority and the timezone overlap you need on a free 30-minute discovery call. No obligation, no sales pitch.

  2. 02Within 24 hours

    Review matched profiles

    We shortlist 2–3 pre-vetted Python engineers from our in-house bench, with CVs and directly relevant work — usually within one business day.

  3. 03Days 1–2

    Interview & test

    Interview each developer yourself, run a paid trial task, or do a live pairing session. You decide who joins — we never push a profile.

  4. 04Within 2 days

    Onboard securely

    Sign a free NDA, then plug the developer into your Jira, repository and Slack. Engagements are month-to-month with no lock-in.

  5. 05Ongoing

    Scale or replace

    Scale the team up or down month by month as the roadmap changes. If the fit is not right, you get a free replacement within the first 30 days.

More backend technologies

Other backend technologies we work with at Krapton.

Three ways to hire Python developers

Pick the engagement that matches how you actually work. No multi-year contracts — scale up or down month by month.

  • Dedicated developer

    Most popular

    Full-time Python engineer who reports only to you. Best for ongoing products, long-term roadmaps and teams that need a core hire without the HR overhead.

    • 40 hours / week
    • Your Jira, your repo
    • Month-to-month

  • Hourly / time & materials

    Pay only for billable hours. Ideal for research spikes, code audits, or variable-load Python work where scope is still being discovered.

    • Weekly timesheets
    • Slack-first comms
    • No minimum commit

  • Fixed-price milestones

    Scoped delivery with clear milestones and acceptance criteria. Best for well-defined Python builds like an MVP, a migration or a specific module.

    • Scope locked upfront
    • Milestone acceptance
    • Predictable budget

Compare ways to hire Python developers

Pick the model that matches how you work. Switch between them as your Python project evolves — no multi-year contracts.

Compare ways to hire Python developers
EngagementDedicated developer (popular)Hourly / time & materialsFixed-price milestones
Best forOngoing products & long-term roadmapsExploratory or variable-load workWell-scoped MVPs, modules & migrations
BillingMonthly, full-time (~160 hrs)Per billable hourPer accepted milestone
CommitmentMonth-to-month, no lock-inNo minimum commitmentScope locked up front
FlexibilityScale up or down monthlyHighest — pay for what you useLowest — defined deliverables
You manageDay-to-day in your toolsWeekly prioritiesAcceptance criteria only
ReportingDaily standupsWeekly timesheetsMilestone demos

Hire Python developers from anywhere

Our New Delhi team partners with founders and engineering leaders across eight key markets, with enough daily timezone overlap to feel in-house.

  • Hire Python developers in India

    Full working-day overlap

    Krapton is an on-shore Python studio in New Delhi. Hire Python developers in India and get senior, full-time engineers at roughly 50–70% of US/UK rates — with English-fluent communication and IST working hours.

  • Hire Python developers in USA

    4–6 hours of daily overlap — your morning, our evening

    Hire dedicated Python developers for your USA team without the local price tag. US companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire Python developers in UK

    Overlap across your whole morning (our afternoon)

    Hire dedicated Python developers for your UK team without the local price tag. UK companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire Python developers in Canada

    4–6 hours of daily overlap with ET and PT

    Hire dedicated Python developers for your Canada team without the local price tag. Canada companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire Python developers in Australia

    Strong overlap — our morning is your afternoon

    Hire dedicated Python developers for your Australia team without the local price tag. Australia companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire Python developers in UAE

    Near-complete overlap — just 90 minutes apart

    Hire dedicated Python developers for your UAE team without the local price tag. UAE companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire Python developers in Germany

    Overlap through your morning and our afternoon (DACH)

    Hire dedicated Python developers for your Germany team without the local price tag. Germany companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire Python developers in Singapore

    Near-complete overlap — only 2.5 hours apart

    Hire dedicated Python developers for your Singapore team without the local price tag. Singapore companies work with our vetted, full-time Python engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

Python developer rates

Hire dedicated Python developers from Krapton's team in India. Rates below are blended hourly bands; dedicated developers are billed monthly with no lock-in.

Get a tailored quote

  • Junior (1–3 yrs)

    Feature work under senior review

    $22–$28/hr

  • Mid-level (3–6 yrs)

    Owns modules end-to-end

    $28–$36/hr

  • Senior / lead (6+ yrs)

    Architecture, reviews, mentoring

    $36–$45/hr

Why teams hire Python developers from Krapton

Projects delivered
50+
In-house engineers
60+
Average client rating
4.8/5
On-time delivery
98%

  • Vetted, full-time engineers

    Every Python developer is a full-time, in-house Krapton engineer — never a freelancer or subcontractor.

  • Ready in 24 hours

    Shortlisted Python CVs within a business day and an onboarded developer within two.

  • Clear hourly rates

    Published hourly bands by seniority, billed monthly, with no lock-in.

  • Your IP, your repos

    Full IP assignment, free NDA on request, and all code in your accounts from day one.

  • 30-day replacement

    Not the right fit? Free replacement within five business days in the first 30 days.

  • Real timezone overlap

    4–6 hours of daily overlap with US, UK, EU, UAE and AU teams for live collaboration.

Services that pair well with Python

Most Python engagements also benefit from these Krapton services. Browse full offerings on the services page.

Hire Python developer

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Hiring Python developers — answered

Practical answers to the questions CTOs and founders ask us most often before they hire.

How quickly can I hire a Python developer from Krapton?

Within 24 hours of the initial call. We keep a bench of pre-vetted Python engineers, so once you confirm the brief we line up two or three shortlisted CVs within a business day and can onboard the selected developer within two.

How much does it cost to hire a dedicated Python developer from India?

Blended hourly rates for senior Python engineers typically fall in the $22–$45 USD range depending on seniority and engagement length. Dedicated developers are billed monthly with no lock-in.

Are your Python developers full-time employees or freelancers?

Every Python developer you hire through Krapton is a full-time, in-house engineer — we never subcontract to freelancers or agencies. That means consistent code quality, signed NDAs, and a single point of accountability for your project.

Who owns the code and IP my Python developers write?

You do — 100%. IP assignment and confidentiality are covered by the contract and a free NDA on request. All code lives in your repositories, on your infrastructure, under your accounts from day one.

What should I look for when I hire Python developers for a backend?

Look for production habits, not just language fluency. A senior Python developer writes typed code, structures projects with clear layers, understands the GIL and when async helps, profiles before optimizing, and tests with pytest against real dependencies. Ask about a framework they know deeply, such as Django’s ORM query behavior or FastAPI’s dependency injection, and how they manage dependencies and packaging with pyproject.toml and a lockfile.

Should our backend use Python or Go?

Use Python when the product depends on data, machine learning or fast iteration, and Go when you need high-concurrency network services with tight latency and memory budgets. Python’s libraries and developer speed usually outweigh its runtime cost for typical SaaS APIs. If one endpoint becomes a bottleneck, it can be optimized or split into a Go service later without rewriting the whole system.

Can you migrate a Flask or legacy Python 2 codebase to a modern stack?

Yes, and the safest route is incremental. For Python 2, the developer first adds characterization tests, then converts with tools such as futurize or python-modernize, resolving bytes-versus-str issues module by module. For Flask to FastAPI or Django, new endpoints are built on the target framework behind the same reverse proxy, and old routes move across one at a time until the legacy app can be retired.

How do you secure and test a Python application?

Security and testing are built into CI rather than left to a final review. Every pull request runs pytest, mypy, Ruff, Bandit and pip-audit; integration tests use Testcontainers so database behavior matches production. On the application side, the developer validates input with Pydantic or Django forms, avoids unsafe deserialization and shell calls, stores secrets in a vault or environment, and keeps framework security settings such as CSRF and secure cookies enabled.

Which Python version should a new project target?

Target a currently supported release, normally the newest version that all your key dependencies publish wheels for. Python 3.13 and 3.14 are solid choices for new work, while anything at or near end of life should be on an upgrade plan. The developer declares the supported range in pyproject.toml, pins the interpreter in the container image, tests the next version in a CI matrix early, and reviews free-threaded builds separately.

Keep exploring

More ways to plan your Python build and team with Krapton.

Ready to build with Python?

Get a free 30-minute consultation with our Python team. Clear roadmap, transparent pricing, no obligation.

  • Free NDA on request
  • Response within 24 hours
  • Certified Python developers
  • Flexible engagement models
  • US, UK, UAE & India clients served

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