How I Built an AI-Powered Job Search Pipeline That Tailors My Resume for Every Application

Parth Parmar — Fri, 17 Apr 2026 04:17:35 +0000

The Problem

Job hunting in 2026 is mostly busywork:

Hours scrolling LinkedIn and job portals
Customizing the same resume 30+ times (same content, different keywords)
Losing track of applications
Missing JD keywords that matter for ATS systems
Cold outreach skipped because researching each company takes too long

I built a tool to automate all of it.

What It Does

Scrape jobs → Filter → Score (AI) → Tailor resume (AI) → Generate PDF → Export Excel → Open top URLs

One command runs the whole pipeline:

python -m src.main --keywords "React Developer" --location "India" --pages 2

How It Works

1. Scraping

Four scrapers (LinkedIn, Internshala, Naukri, Indeed) with a shared base class handling:

Rotating user agents via fake-useragent
Random 1.5-3.5s delays between requests
Full job description fetching from detail pages
Rate limiting to avoid getting blocked

LinkedIn uses their public guest API, Internshala parses HTML, Naukri tries JSON API then falls back to HTML.

2. Filtering

Drops jobs that don't meet your criteria:

Salary below threshold (parses ₹ 4,50,000 - 6,00,000 → 6.0 LPA)
Internships (--exclude-internships by default)
Tags product companies (Razorpay, Swiggy, CRED, etc.) vs service (TCS, Infosys)

3. Scoring with AI

Every job gets sent to Claude with your resume:

{
  "match_score": 87,
  "matched_keywords": ["React", "Redux", "WebSocket"],
  "missing_keywords": ["AWS", "Kubernetes"],
  "recommendation": "Strong Match",
  "reason": "4+ years React experience matches the 3+ requirement..."
}

Two backends:

Anthropic SDK (if you have an API key)
Claude Code CLI via subprocess (if you have Claude Code installed — no API key needed)

4. Resume Tailoring

For top matches (≥70/100), Claude rewrites your resume:

Reorders skills by JD relevance
Rewrites bullets using EXACT JD vocabulary
Injects core_competencies badges for the modern PDF style
Validates structure — can't fabricate skills, can't change company names/dates

5. PDF Generation

Two styles:

Classic: fpdf2, system fonts (Georgia/Times fallback). ATS-safe, plain, small file size.
Modern: HTML + Playwright Chromium. Space Grotesk + DM Sans fonts, cyan-purple gradient header, keyword badges.

6. Excel Export

4-sheet workbook with openpyxl:

All Jobs: sorted by score, color-coded (green ≥70, yellow 40-69, red <40), clickable URLs
Top Matches: ≥70 only, product companies highlighted
Cold Outreach: 20 curated product companies (Razorpay, CRED, Postman, BrowserStack, etc.) with LinkedIn links
Summary: stats, salary distribution, skill gaps

7. One-Click Apply

--open-top 10 opens the top 10 job URLs in your browser. Zero friction to apply.

8. Scheduled Scans

--schedule daily sets up a Windows Task Scheduler entry or crontab line. Fresh postings get fewer applicants — first-mover advantage.

What Works, What Doesn't

Works reliably:

LinkedIn (guest API is stable)
Internshala (HTML scraping, 40 jobs/page)
All AI-powered parts (with API key or Claude Code CLI)

Known limitations:

Naukri often returns 400 (their API is inconsistent)
Indeed returns 403 (Cloudflare anti-bot)
Requests + BS4 can't render JS-heavy pages (would need Playwright scrapers — contributions welcome)

Try It

git clone https://github.com/parth-r-parmar/ai-job-pipeline
cd ai-job-pipeline
pip install -r requirements.txt

# Edit resume.json with your data
python -m src.main --keywords "React Developer" --location "India"

Results end up in output/.

Stack

Python 3.10+ · requests · BeautifulSoup · openpyxl · fpdf2 · Playwright · Anthropic SDK · Claude Code CLI

What's Next

Playwright-based scrapers for Naukri/Indeed
LinkedIn detail pages (currently only getting listing-level data)
Email digest for scheduled runs
Optional browser extension for one-click scraping

MIT licensed. Feedback, issues, and PRs welcome.

Repo: https://github.com/parth-r-parmar/ai-job-pipeline

DEV Community: Parth Parmar