How to get hired
Search "Python developer" and the results mix four jobs. Decide which one you are aiming at before you build a single project, because a Django portfolio does little for a data engineering interview and the reverse is also true.
Backend Python (Django, FastAPI, Flask)
Startups, SaaS companies and fintechs build APIs in Django with Django REST Framework, or in FastAPI for newer services, with Celery for background jobs and PostgreSQL behind them. The usual test is a take-home: build a small REST API with authentication, pagination and tests, in two or three days. Then a review call where they change a requirement and watch you edit your own code. Write tests with pytest, use type hints, and include a Dockerfile; that alone puts you ahead of most submissions.
Data engineering (PySpark, Airflow, Databricks)
Banks such as JPMorgan Chase and Citi, analytics firms such as EXL, and consulting arms hire Python for data pipelines. The tests are SQL first (window functions, joins on messy data, deduplication), then PySpark transformations, then a discussion of how you would schedule and monitor a daily job. "Data Engineer" is the title on the offer, not "Python Developer". If this is your direction, read the data engineering guide too.
Tooling and automation inside engineering companies
Semiconductor and hardware companies, Qualcomm among them, hire Python developers to write test frameworks, build scripts and lab automation around chips and firmware. Electrical and electronics graduates do well here because the job sits next to hardware. Interviews ask Python scripting, file and log parsing with regular expressions, a little Linux, and sometimes C. Titles often say Software Engineer or Senior Engineer with "Python" only in the skills line.
AI and machine learning engineering
The newest titles, AI Engineer and ML Engineer, are mostly for people who already ship Python code: building retrieval systems, calling model APIs, evaluating outputs and serving models. Freshers rarely land these directly. A safer path is a backend or data role first, then an internal move. The data science and AI guide covers the modelling side.
The Python questions that come up in every track
Interviewers in India lean on a familiar set, so prepare them properly rather than memorising one-liners:
- Mutable default arguments, and why def f(x=[]) surprises people
- Lists vs tuples vs sets vs dicts, and roughly how a dict lookup works
- Generators and yield, and when they save memory
- Decorators: write one that times a function
- The GIL: why threads do not speed up CPU-bound Python, and what you use instead (multiprocessing, async for I/O)
- Shallow vs deep copy
- Exceptions and context managers ("with"), and writing your own
For general fresher rounds (aptitude tests, HR, the project walkthrough) see the software fresher interview guide. It covers the common questions; this list is the Python layer on top.
Certifications
Employers in India almost never ask for a Python certificate. A cloud certification (AWS, Azure or Google Cloud) shows up far more often in Python job posts, because most Python work runs on one of them. Spend the time on a deployed project instead.
What the work is like
Backend Python developers spend their days on API endpoints, database migrations and fixing slow queries. Data engineers live in SQL and scheduler dashboards, and their bad days are pipelines that failed at 3 a.m. and a report the business needs by 10. Tooling engineers sit with hardware teams and get pulled into lab debugging.
Pay tends to follow the track rather than the language: data and AI roles at banks and GCCs pay more than backend Python at small startups, and the jump comes when you move from writing scripts to owning a system other people depend on.