Seek solid understanding of core language features (data structures, OOP, decorators, generators), familiarity with common libraries (requests, pandas, pytest), version-control habits (Git), testing culture, and grasp of software design principles. Domain-specific knowledge—web, data science, automation—should align with your project’s needs.

Use a short, real-world coding exercise that mirrors tasks on your team. Ask for clean, tested, well-documented code and discuss their choices in a follow-up review. Supplement with pair-programming or live debugging to observe thought process, adherence to PEP 8, and error-handling habits.

Yes, if your stack relies on them. Django/Flask for web, FastAPI for APIs, pandas/Numpy for data, or asyncio for high concurrency can shorten onboarding. However, strong core language skills and learning agility often outweigh one-to-one framework experience, especially for senior roles.

Present open-ended tasks like building a mini REST client, optimizing an algorithm’s runtime, or refactoring a messy script. Ask them to explain trade-offs, test strategy, and edge cases. Questions on list/dict comprehensions, context managers, and memory profiling also surface depth of knowledge.

Certifications can show initiative but rarely predict day-to-day performance. Prioritize demonstrated project experience, community contributions, and ability to learn over certificates. Use them as a mild positive signal rather than a gatekeeper.