Import Statement Autobiography
Write your career as a list of Python imports, one per line, and the page renders it as a tiny autobiography. import pandas → "Has wrangled data." from django import ... → "Has built for the web." import pytest → "Believes in proof." import openai → "Curious about AI." Add your own; unknown imports get a friendly "a person of many talents". The last line suggests which route your imports point towards. Stored only on your device — and yes, import antigravity gets a special response.
Your career as imports
Readiness checklist for Python developers
For document AI automation
For no-code data products
For LLM (large language model) evaluation
Whichever group you tick most is a sensible first route. Ties are fine; pick the one that sounds more fun.
Sort your tasks
Decide your answer first, then tap a card to check.
Each card\'s back shows where most people placed it.
The three routes, briefly
Document AI automation. Turn messy documents — invoices, forms, receipts — into structured data, with human review for low-confidence fields. Google Document AI's Enterprise OCR (optical character recognition) includes the first 1,000 pages a month free, then a paid tier (checked 23 Oct 2026). "document ai" gets about 2,900 searches a month in India. → Python → document AI
No-code data products. Dashboards and simple data apps for teams who need answers, not notebooks. Google renamed Looker Studio to Data Studio in April 2026; the self-service version is no charge. Streamlit Community Cloud is free for public apps. "looker studio dashboard" still gets about 2,900 searches in India — people use the old name. → Python → data products
LLM evaluation. Measure whether AI systems give good answers — test sets, metrics, regression checks. Open-source frameworks such as DeepEval and promptfoo help. "llm evaluation" gets about 720 searches in India and 1,000 in the US. → Python → LLM evaluation
Also worth a look: AI agent engineer and AI code trainer.
Choosing between them
| If you enjoy… | Try |
|---|---|
| untangling messy inputs | document AI |
| helping non-technical people see clearly | data products |
| proving things work | LLM evaluation |
| building systems that act | AI agents |
By career stage
Early career: data products — quick wins, visible results. Mid: document AI or evaluation. Senior: evaluation leadership and agent architecture value judgement.
Hiring in India and abroad
India: BFSI and logistics firms process huge volumes of documents; agencies and D2C brands need dashboards; AI product teams need evaluation. Global: remote contract work exists for dashboards and document automation; evaluation roles appear at AI product companies. Visa and tax questions are outside our scope.
Your realistic 90 days
You wrote Python that moved data around — Django or Flask apps, scrapers, pandas scripts, ETL (extract, transform, load) jobs on a cron. That's close to three AI-era routes: document AI, LLM evaluation, and small internal data tools. You'll test each on a script you already wrote.
Assumes 2–3 hours a day alongside job applications, a laptop and a phone, and free plans only. Every step has a smaller version and a way to pause. Use them. It runs in three blocks of 30 days, each ending with a signal that you're on track. A signal is not a promise of a job or income.
Worries people bring to this route
- Over 40 in Python? Python's in nearly every AI workflow; experience in clean, tested code stands out.
- A gap. A small dated tool (invoice checker, eval harness) closes it.
- No CS degree. Python hiring is skills-led in most listings we read.
- English. Notebooks and READMEs can be brief.
- Family worries AI writes Python now. Someone still checks it and makes it reliable.
- Runway. Automation gigs can bring early income.
What AI changed in your old tasks
| You used to… | Now you… |
|---|---|
| Parsed PDFs and Excel files with brittle regex | Extract fields with a document AI service and check accuracy |
| Wrote unit tests for your functions | Write evaluation sets that score AI outputs |
| Built ad-hoc pandas reports on request | Ship a Streamlit app people can use without you |
Your first 7 days
| Day | Task | Done when |
|---|---|---|
| 1 | Find 3 old scripts (no client data) and note input, output and what broke | 3 notes |
| 2 | Document AI try-out: extract 5 fields from 10 sample invoices with Google Document AI's free trial or Azure's free tier | Accuracy noted |
| 3 | Eval try-out: install Ragas or write a pytest-style scorer for 10 AI answers | Scores printed |
| 4 | Data-product try-out: wrap one pandas script in a Streamlit app | App runs locally |
| 5 | Score the three on enjoyment and postings seen | Scores |
| 6 | Pick one and rewrite your headline | Saved |
| 7 | Save 10 postings | 10 links |
Days 1–30: Your old script, upgraded
| Step | Time | Done when | Smaller version | If you need to pause |
|---|---|---|---|---|
| Turn one of your 3 scripts into the first proof for the chosen route | about 12 hours over 3 weeks | repo with README and measured results | README + notebook only. | Save or commit what works now and stop adding. A finished small version beats an unfinished big one. |
| Send to 5 ex-colleagues and 1 old manager | about 2 hours | 6 messages | Send to 2 ex-colleagues and 1 old manager. | Optional. If reaching out feels like too much this week, skip it and put the time into your proof. |
Signal by day 30: A data or AI lead replies to your repo link.
Days 31–60: Accuracy, not demos
| Step | Time | Done when | Smaller version | If you need to pause |
|---|---|---|---|---|
| Add a measured result: accuracy on 50 samples, or eval scores before/after a change | about 10 hours | a results table in the README | 20 samples. | Stop at the end of the step you're on and write two lines about where you stopped. Pick it up from those notes later. |
| Apply to 10 roles that mention Python plus your route | about 5 hours | 10 logged | Apply to 5 roles that mention Python plus your route. | Stop sending for now. Keep a list of who you've contacted so you can follow up later without doubling up. |
Signal by day 60: A screening call.
Days 61–90: Interview depth
| Step | Time | Done when | Smaller version | If you need to pause |
|---|---|---|---|---|
| Prepare to explain one error your measurement caught | about 2 hours | 3-minute story rehearsed | Notes. | Optional. Skip it if the week is full; nothing later depends on it. |
| Post the results table with a short explanation | about 2 hours | post live | DM 3. | Write it as a draft and publish later. A saved draft still counts. |
Signal by day 90: A technical second round.
Where the openings are, and what to type
Also worth a weekly look: Instahyre, Cutshort, Wellfound, and iimjobs or hirist for senior roles. Upwork and Contra if you want freelance work.
Startups, data teams and automation shops. LinkedIn, Instahyre and Wellfound carry product roles; Naukri carries services roles; Upwork carries document-automation gigs.
- LinkedIn search string:
Python AND ("document AI" OR OCR OR "LLM evaluation" OR "data apps" OR automation) - Naukri / foundit keywords: Python developer, Python automation, OCR Python
Job-hunt moves that matter for this route
- Search 'Python + LLM', 'document AI', 'evaluation engineer', 'Streamlit' on LinkedIn, Naukri and Instahyre.
- Measured results set you apart from tutorial repos.
- Ex-colleagues in analytics or ML teams are good referrers.
Your 30/60/90 quests
XP: learning 20 · proof 60 · outreach 40. Ranks: Wayfinder → Builder → Pathmaker. Progress stays in your browser only.
A word on Python pay
We haven't verified pay data for these routes.
Scams Python developers see
"Data science job guarantee" courses, fake "AI training" roles with fees, and requests for OTPs or UPI PINs. Official domains only. Report: cybercrime.gov.in / 1930 (India), ReportFraud.ftc.gov (US).
Python developer questions
Do I need machine learning? Not for these routes. Python, data handling and testing matter more. Is no-code a step down? No — choosing the simplest tool that works is a senior skill.
Take the quiz · Related: Backend developer · Java developer
Newer step-by-step plans: Python Developer → AI Agent Engineer and Python Developer → Analytics Engineer, each with one proof project and a 90-day schedule.
Quick quiz: does this path fit?
Answer each, then press Check. Every answer explains itself.
Make your Path Card
A shareable reminder of where you're heading. The day count starts the first time you open this page; quest XP comes from your log above.
Scam check before you apply anywhere
- Real job and gig platforms are free to join. Never pay a "joining" or "training" fee.
- Never share an OTP or UPI PIN — no genuine employer needs them.
- Use official domains only; type the address yourself.
- India: report at cybercrime.gov.in or call 1930.
- US: report at ReportFraud.ftc.gov.
Small steps, every day.
Each quest has a smaller version for hard weeks, and a pause button. Nothing is lost.
Keep exploring
Sources
Checked 23 Oct 2026. We opened every link below ourselves. Search volumes on this site come from OpenSEO keyword research (Oct 2026).
| What we checked | Where |
|---|---|
| Document AI pricing | cloud.google.com |
| Data Studio (formerly Looker Studio) | cloud.google.com |
| DeepEval | github.com |
| Streamlit Community Cloud | streamlit.io |