Hire Python Developers in India | Django, FastAPI, Data & ML Specialists
Python covers four different jobs, and the wrong hire is expensive. Tell us whether you need a web backend, a data pipeline, a machine learning model or a fleet of automation scripts, and we shortlist people who have shipped that specific thing. One monthly figure, from $960/mo, profiles in 48 hours.
Four questions and we can shortlist
Which Python job is this: web, data, ML or automation? Which framework or library set is already in the repo? What seniority? Which hours do you need overlap on? Rita asks those four and the shortlist follows.
No forms. No commitment. Just answers.
What our Python developers actually work on
Python is the most-used language on our bench and the one clients most often brief loosely. "We need a Python developer" can mean four unrelated roles, so this is what the work turns out to be once the calls start.
Web backends and APIs
Django and FastAPI applications serving a web front end, a mobile app or a partner integration. Models, migrations, serialisers, authentication, permissions, background jobs, and the admin screens your ops team lives in. This is the single largest category and usually the one that needs the most product judgement, because the developer is making decisions your users feel.
Data pipelines and reporting
Getting data out of production systems, third-party APIs and spreadsheets, cleaning it, and landing it somewhere a dashboard can read. pandas for the transforms, Airflow or cron for the schedule, and a warehouse at the end. The failure mode here is silent: a pipeline that ran but produced wrong numbers is worse than one that crashed.
Machine learning and AI features
Training a model, or more often taking someone else's model and putting it behind an endpoint your product can call. Classification, forecasting, recommendation, document extraction, retrieval over your own content. Most of this work is not the modelling. It is the data preparation before and the serving, monitoring and retraining after.
Automation and internal tooling
The scripts that replace someone's Tuesday. Scraping a supplier site, reconciling two systems that will never speak to each other, generating the invoices, watching a folder, driving a browser through a form nobody has an API for. Unglamorous, and often the fastest payback on the whole engagement.
If you already know which of the four you need, say so in the first message and skip a round of calls. If you do not, that is a normal place to start and Rita will work it out from what the work has to produce.
Hire a Python web developer, or a Python data engineer?
The same language, two different jobs, and people rarely do both well. Getting this wrong is the most common reason a Python hire underperforms while looking perfectly qualified on paper.
Hire a Python web developer when
There is a product with users. The work is request-response: an endpoint has to return the right thing in under 200 milliseconds, a form has to save, a permission has to hold. The developer needs to think about state, race conditions, database indexes, cache invalidation and what happens when two people click at once.
Ask them to describe an N+1 query they found and how they proved it was fixed. Web developers have that story. Ask about database transaction boundaries and you will hear whether they have been burned before.
Hire a Python data engineer when
The output is a table, a file or a number somebody trusts. The work is batch: it runs at 2am, it processes yesterday, and correctness matters more than latency. The developer needs to think about idempotency, late-arriving records, schema drift, timezones and how to reprocess three months without duplicating rows.
Ask them what happens when their job is run twice by accident. A data engineer answers immediately, because they have written the job that survives it. Ask a web developer and you get a thoughtful guess.
And the two roles Python people are often asked to cover
Machine learning engineer. Overlaps with the data engineer on preparation and with the web developer on serving. If your brief is "build a model", the honest version is usually "build a pipeline, then a model, then an endpoint, then monitoring". A person who has only done Kaggle notebooks will stall at step three. If the modelling itself is the hard part rather than the plumbing, that is a data scientist conversation.
Automation engineer. Scripting, scraping, integrations, glue. Enormously valuable and consistently under-hired, because it never appears on a roadmap. Often the right first Python hire for a company whose real problem is fifteen manual processes rather than a missing product feature.
The Python ecosystem: which specialism you actually need
Python has one language and a dozen more or less separate professional worlds inside it. Below is what each one is, what a developer in it actually produces, and how the front-end and back-end split works when it applies. Name the framework and library set in your first message and the shortlist gets much better.
Hire Python developers in India for Django and Django REST Framework
Django is the batteries-included Python web framework: an ORM, a migration system, an admin interface, authentication, forms, templates and a security posture that ships switched on. Its structure is opinionated, which is why Django codebases written by different teams look alike. Work lands in apps, and inside each app in models, views, serialisers, forms, templates, URLs and migrations. Django REST Framework sits on top and turns models into JSON APIs with serialisers, viewsets, routers and permission classes.
Most people who come to us for a Django Python developer already have an application running and need capacity rather than a rewrite. If you want a frontend Django developer, that is templates, Django forms, the template context, HTMX or Alpine for interactivity, and the admin customisation your operations team asks for weekly. If you want a backend Django developer, that is models and migrations, query optimisation with select_related and prefetch_related, Celery tasks, signals, custom management commands, and REST API development in Django REST Framework with proper serialiser validation and permission classes.
Name your version and your ORM habits. Django 3, 4 and 5 differ in async support, middleware behaviour and default settings, and an upgrade across two major versions is real work rather than a version bump. Say whether you are on the ORM throughout or dropping to raw SQL in places, and whether the admin is an internal tool your staff depend on or something you barely use. Those two answers change who fits.
Hire FastAPI developers in India for high-throughput Python APIs
FastAPI is a modern async Python framework built on Starlette and Pydantic. You declare request and response shapes as typed Python models and it gives you validation, serialisation and OpenAPI documentation from the same declaration. Artefacts are routers, path operation functions, Pydantic schemas, dependency callables and background tasks. Because the type hints are load-bearing rather than decorative, a FastAPI codebase tends to document itself.
People who hire FastAPI developers usually want one of three things: a new service that has to handle real concurrency, an ML model wrapped in an endpoint, or a clean API layer in front of something older. A backend FastAPI Python developer works on routers and dependency injection, async database access, Pydantic v2 validation, auth with OAuth2 and JWT, and the OpenAPI schema your clients generate code from. FastAPI has no template or front-end layer of its own, so pair it with a React developer for the interface.
Async is where FastAPI hires go wrong. A developer who writes async def and then makes a blocking database call inside it has built something slower than the synchronous version, and this is common enough that we test for it. Say whether you are on Pydantic v1 or v2, because the migration between them touched almost every validator, and whether your database driver is async or you are running sync sessions in a thread pool.
Hire Flask developers in India for lightweight Python services
Flask is a microframework: routing, request handling and templating, with everything else chosen by you. There is no built-in ORM, no migration system and no admin, so a Flask project is really Flask plus a set of decisions somebody made years ago. Artefacts are blueprints, view functions, Jinja2 templates, extensions like Flask-SQLAlchemy and Flask-Migrate, and an application factory if the project was set up carefully.
When somebody wants to hire a Flask developer, it is normally to maintain and extend a service that already exists, often one written quickly and now load-bearing. A frontend Flask developer works in Jinja2 templates, WTForms and the static asset pipeline. A backend Flask Python developer works on blueprints, SQLAlchemy models and Alembic migrations, Marshmallow or Pydantic schemas, and REST API development with Flask-RESTful or plain view functions returning JSON.
Tell us how the project is structured. The single-file Flask app and the blueprint-and-factory Flask app are different maintenance jobs, and so is the one where a previous developer used global state that only works with a single worker. If you are considering a move to FastAPI, say so early, because the person who can do both the maintenance and the migration is a narrower search.
Hire Python developers in India for Celery, Redis and background jobs
Anything slow has to leave the request. Celery is the standard Python distributed task queue: you define tasks, a broker such as Redis or RabbitMQ holds them, and worker processes consume them. Around it sit Celery Beat for schedules, result backends, retry policies with exponential backoff, routing across queues, and a dashboard like Flower for visibility. RQ and Dramatiq are lighter alternatives that solve the same problem with less configuration.
A backend Python developer working on Celery writes idempotent tasks, sets sensible time limits and retry rules, splits queues so a slow report cannot starve transactional email, and makes sure a task that fails halfway does not leave a half-written record behind. They also handle the unglamorous parts: what happens when the broker restarts, why a task ran three times, and how to drain a queue that has filled with poison messages.
The commonest defect we see is a task that is not safe to retry. Celery will retry, so a task that charges a card or sends an email without an idempotency key eventually does it twice. Say which broker you use, whether you have Beat schedules, and whether anyone currently watches the failure queue. If the answer to the last one is nobody, that is worth fixing in the first fortnight.
Hire Python developers in India for pandas and NumPy data work
pandas is the dataframe library that most Python data work runs through, built on NumPy's arrays. It reads from CSV, Excel, SQL, JSON and Parquet, and gives you joins, group-bys, pivots, resampling and time-series handling in memory. NumPy underneath provides vectorised numerical operations, which is why correct pandas code is often two orders of magnitude faster than the loop a beginner writes. Polars and DuckDB increasingly appear where datasets outgrow pandas comfort.
A Python developer doing pandas work produces transformation scripts and notebooks that turn messy source data into something dependable: deduplicated, typed, timezone-correct, with the business rules applied once rather than in four places. They write the reconciliation that proves the output matches the source, and the tests that catch it when next month's file arrives with a renamed column.
Ask about memory and about correctness separately. Plenty of people can write a pandas transform on a sample and hit a wall on the real file, so ask what they did when a dataframe would not fit in RAM. Then ask how they validate output, because silently wrong numbers are the characteristic failure of this work. Tell us your data volumes and where the data comes from, and say if this is analysis or a scheduled pipeline, because those are different hires.
Hire scikit-learn, TensorFlow and PyTorch developers in India
These are the three libraries most machine learning in Python is written against. scikit-learn covers classical models, regression, classification, clustering, with a consistent fit and predict interface plus pipelines and cross-validation. PyTorch is the research-default deep learning framework and now the common choice in production too. TensorFlow, with Keras, still runs a large installed base and has the stronger mobile and edge deployment story.
Somebody who wants to hire scikit-learn developers usually has tabular data and a prediction to make: churn, fraud, demand, pricing, lead scoring. That work is mostly feature engineering and honest evaluation, and scikit-learn is the right tool. Deep learning is where PyTorch and TensorFlow come in: images, audio, sequences, embeddings. A backend ML Python developer owns the training script, the experiment tracking, the model artefact, the inference endpoint, and the monitoring that tells you when the model has drifted away from reality.
Be clear whether you need a model built or a model shipped. They attract different people. Ask a candidate how they chose their evaluation metric and what their baseline was, because someone who cannot name a baseline has never had to prove a model was worth deploying. Say what your data looks like and whether inference has to be real time or can run overnight, since that decides the whole serving design.
Hire Python developers in India for LLM, RAG and LangChain work
Most AI features being built now are not trained models. They are applications that call a hosted language model and do careful work around the call. That means retrieval over your own content, chunking and embedding documents into a vector store such as pgvector, Qdrant or Pinecone, prompt construction, tool calling, structured output parsing, streaming responses, and evaluation. LangChain and LlamaIndex are the common frameworks, though plenty of solid production systems use the provider SDK directly and skip the abstraction.
A backend Python developer on LLM work builds the ingestion pipeline that keeps the vector store current, the retrieval layer that finds the right passages, the endpoint that assembles the prompt and streams the answer, and the caching and rate limiting that keep the bill survivable. They also build the evaluation set, which is the part that separates a demo from a feature you can defend when it answers wrongly.
Ask what they measured. This field rewards people who can show a before-and-after on retrieval quality rather than a screenshot of a good answer. Tell us which provider you use or whether you need a self-hosted model, how much content you are retrieving over, and whether the data is sensitive, because that last answer decides the architecture more than anything else.
Hire Python developers in India for Scrapy, Selenium and web automation
When there is no API, Python is how most teams get the data anyway. Scrapy is a full crawling framework with spiders, item pipelines, middlewares and built-in concurrency and throttling. BeautifulSoup and lxml parse the HTML you already have. Selenium and Playwright drive a real browser, which is what you need when the content renders in JavaScript or the workflow involves logging in and clicking through a form.
A Python automation developer produces the scraper, the parsing rules, the retry and rate-limit behaviour that keeps you a well-behaved client, the storage schema, and the monitoring that tells you when a source site changed its markup rather than letting the table quietly stop filling. The same skill set covers internal automation: reconciling two systems, driving a supplier portal that has no API, generating documents, and watching an inbox or folder for work to do.
Scraping breaks, so hire for the maintenance rather than the first run. Ask how they detect a silent failure and how quickly they can repoint a broken selector. We only take on collection from sources you are entitled to collect from, and we will ask about terms of service and robots directives before starting, because the request is often perfectly legitimate and occasionally not.
Hire Python developers in India for SQLAlchemy and database work
SQLAlchemy is the Python data-access toolkit used outside Django: a Core layer for building SQL expressions and an ORM layer for mapping classes to tables, with Alembic handling migrations. Django ships its own ORM instead. Either way the artefacts are the same in spirit: model definitions, relationships, migration scripts, session and transaction management, and the queries that carry your actual business logic.
A backend Python developer working on the data layer designs the schema, writes the migrations that run safely against a table with millions of rows, sets the indexes, and fixes the queries that got slow as the data grew. They read query plans rather than guessing. They also handle the awkward parts an ORM hides: connection pool sizing, transaction scope, lazy loading that fires a query per row, and the SQLAlchemy 1.4 to 2.0 style change that touched how sessions and queries are written.
Say which database and how big. PostgreSQL, MySQL, SQL Server and SQLite behave differently under load and under migration, and a developer who has only ever run migrations against a small database has never had to think about locking. If you have a migration that will need to run without downtime, flag it in the first conversation.
Hire Python developers in India for Airflow and ETL pipelines
Apache Airflow orchestrates scheduled data work. You define a DAG in Python, its tasks and their dependencies, and Airflow handles scheduling, retries, backfills and the record of what ran. Prefect and Dagster are the newer alternatives with a nicer local development story. Around the orchestrator sits the rest of the stack: extraction from source systems and APIs, transformation in pandas or SQL, dbt models if the warehouse does the heavy lifting, and loading into BigQuery, Snowflake, Redshift or plain PostgreSQL.
A Python data engineer on pipeline work produces DAGs whose tasks are idempotent and can be re-run for any date without duplicating data, sensible alerting so a failure reaches a human, data-quality checks that fail the pipeline rather than passing bad numbers downstream, and documentation of where each field came from. That lineage question is the one your finance team will eventually ask.
Ask how they handle a backfill. Someone who has genuinely run Airflow in production will talk about execution dates, catchup behaviour and idempotency without prompting, because those three have bitten them. Tell us your source systems, your schedule, your warehouse, and whether anyone is currently on call when the 2am job fails.
Hire Python developers in India for AWS Lambda, Docker and deployment
Python runs well in short-lived functions and in containers. On the serverless side that is AWS Lambda with API Gateway or EventBridge, Google Cloud Functions or Azure Functions, packaged with the Serverless Framework, AWS SAM or CDK, with cold starts, execution limits, layer sizes and IAM permissions as the constraints you actually design around. On the container side it is a Dockerfile, Gunicorn or Uvicorn workers behind a load balancer, ECS or Kubernetes, and a CI pipeline in GitHub Actions or GitLab CI.
A Python developer handling deployment writes a Dockerfile that produces a small image and does not run as root, configures workers to match the workload rather than copying a blog post, sets up structured logging and health checks, manages secrets outside the image, and builds the pipeline that runs tests before anything ships. On Lambda they keep the package lean, handle the retry semantics of whatever invoked the function, and stop a cold start from becoming a user-visible timeout.
Say who owns infrastructure today. If nobody does, a senior Python developer can carry it for a while, but past a certain size that becomes a DevOps engineer conversation rather than something to bolt onto a backend role. Tell us your cloud and whether you have Terraform, because inheriting undocumented click-built infrastructure is slower work than people expect.
Hire PyQt and Python desktop developers in India
Not everything is a web application. PyQt and PySide wrap the Qt toolkit and are how most serious Python desktop software gets built: native widgets, a designer file, signals and slots for event handling, threading so the interface does not freeze while work happens, and PyInstaller to produce something a user can install. Tkinter ships with Python and is fine for internal tools. Kivy targets touch and mobile.
People who hire a PyQt developer usually have laboratory equipment, an engineering tool, a manufacturing station or an internal application that has to run offline on a specific machine. A PyQt Python developer builds the windows and dialogs, wires them to the model layer, keeps long-running work on a worker thread, handles local storage in SQLite, and produces the packaged installer with the signing and update path sorted out.
This is a narrower bench than the web roles, so give us more notice. Say which Qt binding and licence you are on, since PyQt and PySide differ there in ways that matter commercially, which operating systems you ship to, and whether the application talks to hardware. Serial ports and instrument drivers are a specific skill and worth naming up front.
Do not see your library? The pattern above holds for the rest of the ecosystem too, from Streamlit and Jupyter through OpenCV, Pillow, boto3, Requests, httpx, pytest and Pydantic. Name what is in your requirements file and we will match against it.
What your Python developer will do once you hire them
Concretely, day to day. This is the work a dedicated Python developer takes off your team's plate, in roughly the order it tends to arrive.
Read the codebase and the data model first
Before writing anything, they walk the repository, the requirements file, the migrations and the database schema, and write down what they found. Python projects accumulate two ways of doing the same thing, and knowing which one is current saves a fortnight of arguing in code review.
Build and ship features against your board
The bulk of the job: pick up a ticket, design the model change, write the endpoint or the transform, write the tests, raise the pull request. They work in your sprint cadence and your branching model rather than importing their own.
Design and review the database schema
New tables, new columns, the relationships between them, and the migrations that apply the change safely against production data. On an established database they will also flag the denormalisation that is now causing you bugs, and propose a path out that does not need a weekend of downtime.
Build and document REST or GraphQL APIs
Endpoints with consistent naming, real input validation, sensible status codes, pagination, versioning and authentication. In FastAPI the OpenAPI documentation comes from the code, so it stays true; in Django REST Framework they keep the schema generated rather than hand-written for the same reason.
Move slow work into background jobs
Report generation, bulk email, file processing, third-party calls that occasionally hang. They identify what is making requests slow, move it to Celery or RQ with retries and idempotency, and add the visibility that tells you when the queue is backing up.
Write tests, and make them run in CI
pytest with fixtures and factories, unit tests around business rules, integration tests through the API, and coverage that is honest about which paths matter. Then wire it into GitHub Actions so a broken branch fails before review rather than after deployment.
Find and fix the performance problems
Profile rather than guess: query counts per request, the N+1 loops an ORM hides, missing indexes, serialisation overhead, memory that grows across a batch. They come back with a measurement before and after, not an assertion that it feels faster.
Integrate the third-party services you depend on
Payments, shipping, CRM, email, storage, accounting, whatever your product has to talk to. The work is mostly in the failure paths: timeouts, retries, webhook verification, replayed events, and reconciliation when the other side disagrees with you about what happened.
Automate the manual processes around the product
The spreadsheet somebody updates every Monday, the export that gets emailed to a partner, the reconciliation between two systems. These are small scripts with a large payback, and a Python developer will usually spot three of them in the first month without being asked.
Handle the security work properly
Parameterised queries, output escaping, CSRF protection, authorisation checked on the server rather than hidden in the interface, secrets in environment configuration rather than the repository, dependency vulnerabilities patched. If a penetration test has produced a report, they work through it item by item.
Upgrade Python and the dependency tree
Python 2 to 3 migrations still exist, and so do applications pinned to a Django or Flask release that no longer receives security fixes. They plan the upgrade in stages, get the test suite green at each step, and keep the application shippable throughout rather than disappearing for a month.
Leave documentation and handover behind
A README that gets a new developer running locally, notes on the deployment path, an architecture sketch, and comments that explain why rather than what. This matters most in the case nobody plans for, which is the developer moving on.
Does a Python developer do your front end?
Sometimes, and it depends entirely on which framework you are on. This is worth settling before you hire, because the assumption is usually unspoken on both sides.
On Django or Flask, often yes
Both ship a template layer, and a server-rendered Django or Flask application is a complete web application. A capable Python developer will write the templates, style them adequately, and add interactivity with HTMX, Alpine.js or plain JavaScript. For an internal tool, an admin panel or a content-driven site, that is genuinely all you need and adding a separate front-end framework would slow you down.
On FastAPI, no, by design
FastAPI is an API framework. There is no template layer to speak of, so the interface is a separate application talking to it over HTTP. That is usually React or Next.js, sometimes Vue. If your brief is FastAPI plus a polished product interface, you are hiring two roles, and pretending otherwise leads to a back end you like and a front end nobody wants to touch.
Where the honest limit sits
A Python developer who says they do front end usually means they can implement a design competently in templates. They are generally not a designer, and they are generally not the person to build a complex single-page application with its own state management, component library and accessibility standard. Ask to see something they built end to end and judge the interface yourself.
If the front end is the product, pair the Python developer with a React developer and let each work where they are strong. If you would rather one person carried both, that is a full-stack developer brief and we will search for it as one.
Where to hire Python developers: three channels compared
Python is the most crowded hiring market of any language, which cuts both ways: a deep pool, and a lot of people whose experience is a course rather than a production system. Each channel fails differently.
Freelance marketplaces
Right for a bounded script, a scraper, a one-off migration or a proof of concept. You can be started within a day and the commercial risk is small.
Wrong for anything continuing. Attention is split across clients, context is rebuilt weekly, and the person who wrote your Airflow DAG is unreachable when it fails in March. Python attracts an especially wide skill spread here, so vetting effort falls entirely on you.
Direct in-house hire
The right answer when Python is core to your product and you want the knowledge permanently inside the company. Full control, full commitment.
Slow and front-loaded. Sourcing, screening, several interview rounds, notice period, and a market where the good candidates hold several offers. On US figures you are also carrying benefits at 47.7% of wages and an average cost-per-hire of $5,475 before anyone writes a line.
Dedicated developer through us
One vetted engineer, full-time, on your board and in your standups, with payroll, compliance, equipment and leave cover on our side. Profiles in 48 hours because the vetting already happened.
The honest limitation: they are employed by us, not you, so equity and a permanent seat are not on the table. If what you need is a founding engineer, hire directly. If what you need is capacity that starts this month and can scale either way, this is the cheaper and faster route.
Still weighing the first against the third? We wrote the comparison out properly in freelancers vs dedicated remote teams.
How to hire Python developers: what to look for and how to test it
Python is easy to look competent in and hard to be genuinely good at, because the language forgives things that later cost you. These are the checks that separate the two, and they work whether you hire through us or not.
Give them a codebase to read, not a puzzle to solve
Send 200 lines of imperfect Python and ask what they would change and in what order. You learn more in twenty minutes than from any algorithm exercise: whether they notice the mutable default argument, the bare except, the query inside the loop, and whether they can prioritise rather than list every flaw at equal weight.
Ask what they would do about the database
Describe a table that has grown to ten million rows and a page that has become slow, and listen. A strong candidate asks about query plans, indexes and access patterns before proposing anything. A weaker one reaches immediately for caching or a rewrite. Both answers can be right, but only one comes after diagnosis.
Test the specialism you are actually buying
A Django question does not tell you whether somebody can build a reliable Airflow DAG, and an ML question does not tell you whether they can ship an endpoint. Test the thing the job needs. For data roles, ask what happens when the job runs twice; for web roles, ask about transaction boundaries; for ML roles, ask about the baseline and the metric.
Look for tests in what they show you
Ask for a repository they are proud of and look at the test directory first. pytest fixtures, factories, tests around the business rules rather than the getters. Someone who tests habitually writes different production code, and you can see it in the shape of their functions.
Ask about a failure they caused
The pipeline that produced wrong numbers for a week, the migration that locked a table, the retry that double-charged. The answer tells you whether they have operated software rather than only written it, and whether they will tell you early when something goes wrong. That second thing matters more remotely than locally.
Check they can write, not just code
Remote work runs on written English: pull request descriptions, standup notes, an explanation of a trade-off your non-technical stakeholder has to accept. Ask them to write a short summary of a technical decision. Every developer we put forward has passed written and verbal English assessment, and this is why.
Our 4-stage Python developer vetting process
Roughly 8% of Python applicants reach a client shortlist. The bar is highest at stage two, because Python attracts a lot of tutorial-deep candidates and a written challenge finds them quickly.
History and code review
Project history by specialism, GitHub and PyPI contributions, and what they actually owned versus assisted on. We separate web, data, ML and automation experience at this stage rather than later.
Role-specific technical test
A Django or FastAPI endpoint with tests, a pipeline that has to be safe to re-run, or a model with an honest evaluation, depending on the track. Submitted as a repository with a README, judged the way a pull request would be.
Live interview with a senior engineer
We walk their submission with them: why this structure, what would break at ten times the load, what they would change now. Plus a live debugging exercise, because reading a traceback under mild pressure is the job.
Background and communication check
Identity verification, employment history, references from a previous lead, written and verbal English assessment, then NDA and IP assignment signed before any client introduction.
You still interview. Our vetting removes the bottom of the market so your time goes on the two or three people who could actually do the job, and you keep the decision.
Python developer cost: US, UK and Australia vs India
Base salary is the number people compare and the wrong one. Below are the published figures from each market's official statistics agency, with the methodology attached, because you will be asked to defend these internally.
| Market | Occupation as published | Median annual | Source |
|---|---|---|---|
| United States | Software Developers, SOC 15-1252 | $135,980 | BLS OEWS, May 2025 |
| United States | Data Scientists, SOC 15-2051 | $112,590 | BLS OEWS, May 2025 |
| United Kingdom | Programmers and software development professionals, SOC 2134 | £56,914 | ONS ASHE 2025, provisional |
| United Kingdom | IT business analysts, architects and systems designers, SOC 2132 | £56,271 | ONS ASHE 2025, provisional |
| Australia | Business and systems analysts, and programmers, ANZSCO 261 | A$139,776 | ABS Employee Earnings and Hours, May 2025 |
| India | Software Engineer, average CTC (690k sample) | ₹9.6 lakh | AmbitionBox, self-reported, July 2026 |
Methodology. The US software developer figure comes from a government survey covering 1,687,890 employed developers, with a 25th percentile of $105,210 and a 90th of $214,670. That spread inside a single market is wider than most India comparisons admit, and it matters here because Python spans both the software developer and data scientist classifications. UK data is the Annual Survey of Hours and Earnings 2025, provisional, released October 2025, with a revised set due late 2026. The Australian figures are full-time non-managerial employees at the adult rate, the correct basis for like-for-like comparison. The India number is self-reported average cost-to-company and carries the widest error bar of the six: Stack Overflow's 2025 survey puts India back-end pay at roughly $22,086 on a sample that skews junior, while Levels.fyi's India software-engineer median of about $31,432 reflects big-tech and global-capability-centre pay rather than the wider market. Publish India as a range with the sample named, never as a single multiple.
The load matters more than the salary gap. US employer benefit costs run at 47.7% of wages for professional and related occupations, per the BLS Employer Costs for Employee Compensation release of March 2026. Add agency placement at 15–25% of first-year salary, six to ten weeks of vacancy while the seat is empty, then equipment, tooling and workspace. Our dedicated monthly figure starts from $960/mo and covers recruitment, vetting, payroll, statutory compliance, HR, equipment, workspace and cover during leave. We publish the anchor rather than a full card because the right number depends on seniority, specialism and shift.
Indian salaries are rising, so model for it
Aon's survey put actual Indian pay growth at 8.9% in 2025 with 9.1% projected for 2026, and Deloitte India's Talent Outlook 2026 arrives at the same 9.1%. Python and data roles sit at the faster end of that distribution, because domestic demand for them is strong. A three-year business case with a flat India rate is wrong, and any vendor whose pitch depends on the gap never narrowing is not being straight with you.
Pairing matters on Python projects. An interface in front of a FastAPI service is usually a React developer; a JavaScript service alongside the Python one is a Node.js developer; modelling rather than plumbing is a data scientist. Weighing this against a contractor first? Read freelancers vs dedicated remote teams.
Hiring a dedicated Python team rather than one developer
Past a certain scope one Python developer becomes the constraint, usually when the same person is expected to own the API, the pipeline and the deployment. These are the shapes that work.
Backend developer plus QA
The commonest first expansion, and the cheapest quality improvement available. The developer stops being the only person who tests their own work, regression coverage grows, and release confidence goes up within a sprint or two. Add a QA engineer before adding a second developer.
Web developer plus data engineer
The right split when your product has both an application and a reporting or pipeline obligation. Two people who each stay in their strong lane will out-deliver one generalist doing both badly, and the handover between them is a clean interface rather than a context switch.
Pod with a tech lead
Three to six people where a lead owns delivery, runs code review inside the pod and reports to you rather than sending you every decision. Right when you do not have engineering management capacity in-house and do not want to acquire it just to run this work.
Seats move monthly in either direction, so start with the smallest shape that could work. Build your team puts a shape and a figure in front of you without a call.
Python developer engagement models
Three ways to buy the same vetted bench. Public anchor is from $960/mo; the full card by seniority and specialism comes from Rita or Build Your Team.
Dedicated Python developer
One engineer, 160 hours a month, yours alone. On your board, in your repo, reporting to your lead. The right default when there is a roadmap rather than a task list.
- Codebase and data model reviewed before day one
- Shift matched to your market
- 30-day free replacement
- 30-day notice to scale down
Dedicated Python team
Two to six people with a lead who owns delivery. Developer plus QA is the usual starting shape; a data engineer joins when the pipeline work stops being a side task.
- Tech lead included
- Code review inside the pod
- Add or drop seats monthly
- Shared sprint cadence with your team
Hourly and sprint-based
Bounded work with a defined end: a Python 3 migration, a Django version upgrade, a scraper that needs rebuilding, a slow-query investigation, launch-week cover. Minimum 20 hours a week, time-tracked.
- No monthly commitment
- Agreed target up front
- Weekly time reports
- Converts to dedicated any month
Compare structures in detail: staff augmentation, dedicated teams, hourly engagement.
Working with a remote Python developer
The mechanics that decide whether this works. None of it is Python-specific, and all of it is where remote engagements actually fail.
Overlap hours, agreed in writing
Four to six hours against US Eastern, UK or Australian Eastern time, fixed before the start date rather than negotiated in week three. Indian Standard Time overlaps the UK afternoon comfortably, the US morning with an early or late shift, and Australia almost fully.
Access sorted before day one
Repository, issue tracker, staging environment, database (anonymised where it holds personal data), cloud console with least privilege, and the credentials to run the test suite locally. A developer waiting on access for a week is the most avoidable cost in this whole arrangement.
Written by default
Decisions in the ticket, not in a call nobody minuted. Pull request descriptions that explain the trade-off. A short written standup so the person in the other timezone can read it when they start rather than waiting for a meeting.
Someone on your side owns review
Every pull request needs a reader with the authority to merge. Without that, work queues up invisibly and both sides conclude the other is slow. If you have nobody, take the pod option and let our tech lead carry review inside the team.
Contracts, IP and data handling
NDA and IP assignment signed before introduction, so everything written for you is yours. Where the work touches personal data we will discuss GDPR posture, data residency and what may be copied to a local machine before anyone connects.
Leave, cover and the replacement clause
Indian public holidays differ from yours, so the calendar is shared up front. Leave cover comes from the same bench. If the fit is wrong, replacement inside the first 30 days costs nothing, and the second developer gets a proper handover rather than starting cold.
Python skills and expertise we cover
Name what is in your requirements file. If something is missing from this list, it is worth asking, because the bench is broader than the tags.
Sample Python developer profiles
Representative of the bench rather than a live list, because availability changes weekly. Ask Rita for current profiles matching your specialism and shift, with the rate against each.
Four multi-tenant SaaS products taken from first commit to production for UK clients. Strong on query optimisation and on migrations that run against a live database without locking it. Has led two Django major-version upgrades.
Async FastAPI services and the ETL behind them for Australian clients. Comfortable putting a model behind an endpoint and keeping the pipeline that feeds it honest. Writes idempotent DAGs by habit rather than after being asked.
Clean Flask APIs and scrapers with real test coverage and sensible retry behaviour. Available on a US Eastern overlap shift. Careful, asks good questions early, and documents as they go.
Industries our Python developers have served
Domain context shortens the ramp, particularly where the rules are regulatory rather than technical.
FAQs about hiring Python developers in India
Tell us four things: which Python job this is (web, data, ML or automation), which frameworks and libraries are already in the repository, the seniority you need, and the hours you need overlap on. We shortlist from an already-vetted bench and send two or three profiles within 48 hours. You interview and you choose. There is no placement fee, no lock-in contract, and a free replacement inside the first 30 days if the fit is wrong.
Dedicated Python engagements start from $960/mo all-in, covering recruitment, vetting, payroll, statutory compliance, HR, equipment and workspace. The figure rises with seniority and specialism, since a senior data or ML engineer costs more than a mid-level Django developer. We publish the starting anchor rather than a full rate card because the right number depends on those variables; ask Rita or use Build Your Team for the complete card.
A US software developer earns a median $135,980 a year and a US data scientist $112,590, per the BLS Occupational Employment and Wage Statistics for May 2025. UK programmers and software development professionals sit at a median of £56,914, per ONS ASHE 2025 provisional. On top of US salary, employer benefits add roughly 47.7% of wages for professional occupations (BLS ECEC, March 2026), and average cost-per-hire is $5,475 (SHRM 2025 Benchmarking Reports). Compare fully-loaded cost against our monthly figure rather than salary against salary.
Decide by what the work has to produce. If the output is a product users interact with, hire a web developer who thinks in requests, transactions and database indexes. If the output is a table, file or number somebody trusts, hire a data engineer who thinks in idempotency, backfills and schema drift. Very few people are genuinely strong at both, and hiring the wrong one is the most common reason a well-qualified Python developer underperforms.
Yes, and it is a common request. FastAPI work means routers and dependency injection, Pydantic v2 schemas, async database access, OAuth2 and JWT authentication, and the generated OpenAPI schema your clients build against. Tell us whether you are on Pydantic v1 or v2 and whether your database driver is async, because both change who fits. We test specifically for developers who write async code that is actually non-blocking, which is the usual defect in this area.
We have Flask developers. Most Flask requests are maintaining and extending a service that already exists and has become load-bearing, which needs someone comfortable reading other people's decisions rather than imposing their own. Tell us how the project is structured, because a single-file Flask app and a blueprint-and-factory app are different maintenance jobs. If you are considering a move to FastAPI, say so early so we shortlist for both.
Yes. That means Airflow, Prefect or Dagster for orchestration, pandas or SQL for transformation, dbt where the warehouse does the heavy lifting, and loading into BigQuery, Snowflake, Redshift or PostgreSQL. The developers we put forward for pipeline work write DAGs that are safe to re-run for any date without duplicating data, add data-quality checks that fail the run rather than passing bad numbers downstream, and set up alerting that reaches a human.
Yes, across scikit-learn for classical and tabular problems, and PyTorch or TensorFlow for deep learning. Be clear about whether you need a model built or a model shipped, because those attract different people. Most production ML work is data preparation before and serving, monitoring and retraining after, rather than the modelling itself. If the modelling is genuinely the hard part, a data scientist may be the better brief.
Yes. In practice that means document ingestion, chunking and embedding into a vector store such as pgvector or Qdrant, a retrieval layer, prompt construction with structured output, streaming responses, and caching and rate limiting to keep costs predictable. LangChain and LlamaIndex are both in use, and so is calling the provider SDK directly. Tell us which provider you use, how much content you retrieve over, and whether the data is sensitive, since that decides the architecture.
Yes, and scikit-learn is usually the right tool when your data is tabular and the task is churn, fraud, demand, pricing or lead scoring. Most of the work is feature engineering and honest evaluation rather than model selection. Ask any candidate what their baseline was and how they chose their metric; someone who cannot name a baseline has never had to prove a model was worth deploying.
Yes, though this is a narrower bench than the web roles, so give us more notice. PyQt and PySide work covers windows and dialogs, signals and slots, worker threads so the interface stays responsive, local SQLite storage, and packaging with PyInstaller. Tell us which Qt binding and licence you are on, which operating systems you ship to, and whether the application talks to hardware, because serial ports and instrument drivers are a specific skill.
On Django or Flask, usually yes, because both ship a template layer and a server-rendered application is complete. Expect competent templates with HTMX or Alpine.js for interactivity rather than design work. On FastAPI, no: it is an API framework with no template layer, so the interface is a separate React or Vue application. If the front end is the product, pair the Python developer with a React developer instead of stretching one person across both.
Yes. Python 2 to 3 migrations still come up, and so do applications pinned to a Django or Flask release that no longer receives security fixes. The approach is staged: get the test suite green, upgrade in steps, keep the application shippable throughout. Ask for the upgrade plan before the work starts, and be wary of anyone who proposes a rewrite before they have read the codebase.
Replacement inside the first 30 days costs nothing, and the second developer gets a proper handover rather than starting cold. After that, 30 days' notice scales the engagement down with no severance. In practice most mismatches surface in the first fortnight and are about specialism rather than ability, which is why we push so hard on naming the Python job precisely before shortlisting.
Get matched with a Python developer in 48 hours
Tell Rita which Python job this is and which libraries are in your requirements file. Profiles follow within two working days, from $960/mo all-in.