Python has quietly become a stricter language in practice. A modern Python codebase carries type hints checked in CI, validates incoming data with Pydantic, lints with Ruff and often runs on FastAPI, so the loose, script-style Python that once passed for experience now looks out of place. At the same time, AI assistants write much of the routine code, and Python developers are increasingly the people asked to wire LLM calls into a product. The Python developer skills that matter have moved towards reading and judging code, and towards keeping a service correct once real users and real data hit it.
This guide is for recruiters and engineering managers working out what to test. It sets out the core skills, each with a quick way to test it, then the habits that distinguish strong developers, what is gaining and losing ground, a matrix by level and an assessment process that avoids the usual keyword trap.
Core technical skills for a Python developer
The language itself
Good developers understand how Python actually behaves: names and objects, mutability, iterators and generators, context managers, dataclasses and exceptions. How to check it: show a short function with a mutable default argument, call it a few times, and ask them to predict the output before running it. Someone with real experience explains the shared list straight away and knows the fix.
Type hints and data validation
Typed Python is now the norm in serious codebases, checked with mypy or Pyright, and Pydantic is the common way to validate data at the edges of a system. Good developers validate once at the boundary and pass well-typed objects inside. How to check it: give them a handler that takes a raw dictionary from a webhook and indexes into it freely, then ask how they would make it safe. Look for a Pydantic model, clear error responses and no repeated key checks scattered through the code.
Framework depth in Django, FastAPI or Flask
Knowing a framework means more than building a CRUD screen. In Django it covers migrations, the admin, signals and when to avoid them. In FastAPI it covers dependency injection, request models and background tasks. How to check it: ask how they would add a required column to a large, busy table through a migration without taking the service down. Good answers add the column as nullable, backfill in batches and only then enforce the constraint.
Databases, transactions and race conditions
Most production bugs in Python backends sit in the data layer. Strong developers understand transactions, indexes and isolation, and they know tools such as select_for_update in Django or row locks in SQLAlchemy. How to check it: describe two users trying to book the last available slot at the same moment, and ask how the code should stop a double booking. Answers that rely on checking first and then writing, without a lock or a constraint, miss the problem.
API design and third-party integrations
Python developers spend a lot of time talking to other systems. Good ones design consistent endpoints with sensible pagination and errors, and treat every outbound call as something that can be slow, fail or arrive twice. How to check it: ask how they would build a webhook receiver for a payment provider such as Razorpay or Stripe. Listen for signature verification, safe handling of duplicate events and replying quickly while the real work goes to a queue.
A sensible testing strategy
Writing tests is common; knowing what they prove is less so. Good developers mix fast unit tests with a smaller number of tests against a real database, often using Docker or Testcontainers, and use Hypothesis for property-based tests where inputs vary widely. How to check it: show a test file that mocks every dependency of the function under test, and ask what the tests actually guarantee. A strong candidate points out that they mostly confirm the mocks were called.
Profiling and observability
When a service slows down, guesswork wastes time. Good developers profile with cProfile or py-spy, log in a structured way, and use error tracking such as Sentry or tracing through OpenTelemetry. How to check it: ask what they would put in place before a new service goes live so that a future slowdown can be diagnosed. Expect request timing, useful log fields and alerts on error rates, not “we would add logging if needed”.
Building LLM features into products
Many Python roles now include calling LLM APIs: generating text, extracting structured data or answering questions over a company’s own documents with retrieval. The engineering skill lies in handling slow and unreliable responses, enforcing structured outputs and keeping costs visible. How to check it: ask what their code does when the model returns malformed JSON or times out in the middle of a user request. Good answers cover validation with a schema, a limited retry and a fallback the user can live with.
Working skills that separate good from average
- Working safely in code they did not write. Most Python jobs involve an existing codebase. Good developers read before they change, follow the conventions already there and make small, reviewable changes instead of rewriting modules they do not yet understand.
- Useful code review. A strong developer leaves review comments that explain the risk, separates must-fix issues from preferences and takes feedback on their own pull requests without defensiveness.
- Matching the engineering to the job. A one-off data fix does not need a service with full test coverage, and a payment integration should not be a script run from someone’s laptop. Good developers judge how much structure a task deserves and say so.
- Understanding the people who use the tool. Much Python work automates something an operations or finance team did by hand. Developers who sit with those users first build tools that match how the work is really done.
Skills gaining and losing ground in 2026
Gaining ground. With AI assistants producing much of the boilerplate, reviewing generated code has become a core skill, including checking that a suggested package actually exists and is the one intended, since attackers now register look-alike names. Supply chain care more broadly, through lockfiles, pinned versions and tools such as pip-audit, matters more than it did. Typing and validation are expected rather than admired. Concurrency judgement is getting more attention as async frameworks spread and recent CPython releases offer an optional free-threaded build. LLM integration has moved from a specialist task into ordinary backend work.
Losing ground. Python 2 migration experience is now rarely relevant. Hand-writing boilerplate CRUD views, serialisers and setup scripts counts for less when frameworks and assistants produce them, and pyproject.toml with tools such as uv or Poetry has replaced most older packaging conventions. Recalling standard library details from memory carries little weight in an interview when the real work happens with documentation open.
Python developer skills matrix by level
| Skill area | Junior | Mid-level | Senior |
|---|---|---|---|
| Core Python | Fluent syntax and standard library | Understands the data model and its pitfalls | Teaches idioms and sets team conventions |
| Typing and validation | Adds hints to new code | Validates at boundaries with Pydantic | Sets typing policy for the codebase |
| Frameworks | Adds views, routes and models | Owns a service and its migrations | Chooses frameworks and service structure |
| Databases | Writes ORM queries correctly | Handles transactions, indexes and locking | Designs schemas for growth and safety |
| APIs and integrations | Calls APIs with timeouts | Builds robust webhooks and retries | Defines API standards across teams |
| Testing | Writes unit tests with pytest | Balances unit and integration tests | Sets the testing strategy and CI gates |
| Operations | Reads logs to find errors | Profiles and fixes slow paths | Plans observability and incident response |
| Collaboration | Responds well to review | Gives clear, kind reviews | Mentors and shapes engineering culture |
How to assess Python developer skills in a hiring process
Python CVs tend to list every library the person has ever imported, so the process has to show judgement rather than exposure. If you still need to settle the scope of the role, our Python developer job description covers that, and the steps below cover the testing.
- A written pull request review. Share a small pull request with planted problems: SQL built with string formatting, an outbound call with no timeout, a secret in the code and a bare except. Ask for review comments as they would leave them. You see security sense and tone together.
- A change to an existing repository. Give them a small working project with tests and ask for one new feature. A greenfield task cannot show whether someone respects existing structure, but extending working code does.
- A debugging session. Hand over a failing test and its traceback and watch how they investigate. Good developers read the traceback carefully and narrow the problem down before editing anything.
- A conversation about AI assistants. Ask which parts of their work they hand to an assistant and how they check what comes back, including new dependencies it suggests.
Red flags on a CV: a long list of unrelated libraries, from TensorFlow to Selenium, with no product attached; experience limited to scraping or one-off scripts for a backend role; no sign of tests, code review or deployments anywhere; and portfolio projects that are only course clones such as a to-do app or a blog.
Frequently asked questions
Which other languages should a Python developer know?
SQL is close to essential, because almost every Python backend talks to a relational database. Basic shell scripting helps with deployment and debugging. Some JavaScript or TypeScript is useful when the developer works closely with a frontend.
Do Python developers need Docker and cloud skills?
They should be able to run their service in a container and understand how it is deployed, configured and monitored. Deep infrastructure work can sit with a DevOps or platform team, but a developer who cannot read a Dockerfile will struggle with modern deployment.
Can a Java or JavaScript developer move into a Python role?
Usually, and the syntax is the easy part. The adjustment is in idioms, packaging and the frameworks. A strong developer from another language can be productive in Python fairly quickly if the team has good review habits.
How do you test skills when a candidate cannot share employer code?
Most professional code is private, so do not penalise that. Use a short exercise built around your own domain, ask them to talk through the design of a system they worked on without showing code, or review open-source contributions if they have any.
For developers who can show these skills in practice, Zilo AI can help you hire Python developers in India as contract, contract-to-hire or permanent staff, and our IT staffing page covers the frontend, backend and data roles around them. Contact our team with your framework, your codebase and the work planned.