Python script failing in production.Fixed in 1 hour.

PM assigned in 10 minutes. Python engineer starts diagnosing within the hour using your Django error logs, Sentry traces, or CloudWatch. Django 500 errors in production are typically caused by: an unhandled exception from a model field change that was not properly migrated (IntegrityError or FieldDoesNotExist), a missing environment variable after deployment breaking a settings import, a database connection timeout from a slow ORM query introduced in the last release, or a third-party library version conflict after a requirements.txt update. The engineer traces the exact traceback, identifies the root cause, applies the fix, runs your test suite, and deploys through your existing CI/CD pipeline before the session closes.

Yes. The engineer works directly in your repository using your existing development workflow your Git branching strategy, your virtual environment setup, your Django project structure. We do not impose a new architecture or rewrite anything that does not need changing. For deployment, the engineer works with whatever you use: Heroku, Railway, AWS EBS, EC2, or Docker on Kubernetes. The PM reviews the engineer's changes for code quality and Django best practices before they are merged following your existing conventions for models, views, serializers, and URL routing. All changes go through your standard PR review process; the PM is the technical reviewer on the QuickHire side.

Yes. Celery integration task queuing, periodic tasks with Celery Beat, Redis or RabbitMQ broker configuration, and worker scaling is a common engagement. Engineers handle: Celery task failures and retry logic (exponential backoff, max retries, dead letter queues), task result storage and expiry configuration, Flower monitoring setup, worker memory leaks from tasks that do not release resources, and task idempotency issues causing duplicate processing. For FastAPI async applications, engineers work with asyncio, async SQLAlchemy, Pydantic v2, and async task handling with Celery or ARQ. PM confirms the specific async framework and broker before the session starts.

ML pipeline debugging is in scope PM matches an engineer with the specific combination you need. For Python ML pipeline issues: scikit-learn pipeline failures from feature dimension mismatches, PyTorch or TensorFlow training loop errors (NaN loss, out-of-memory during batch processing, GPU allocation failures), data preprocessing bugs from pandas dtype coercions or missing value handling edge cases, and MLflow or DVC pipeline run failures. For serving infrastructure: FastAPI or Flask model serving latency, batching logic for throughput optimisation, and memory management for large model weights. PM assesses whether the issue requires a data engineering specialist, an ML engineer, or a Python backend engineer, and matches accordingly.

Every QuickHire Python engineer passes a live debugging exercise on a broken Django or FastAPI application real environment, real database, time-limited. They must identify the root cause and implement a fix without hints. The written assessment covers: Django ORM query optimisation (select_related, prefetch_related, annotate vs aggregate), async Python patterns (asyncio event loop, coroutine pitfalls, context manager usage), SQLAlchemy and Alembic migration patterns, Python packaging (pyproject.toml, dependency resolution), and testing with pytest (fixtures, parametrize, mocking). Portfolio review focuses on production Python systems with measurable performance impact. Only the top 3% of applicants pass all stages. PM matches specifically to your framework, ORM, and Python version.