How to Scrape CutShort Jobs for India Tech Hiring Data (No API)

If you are tracking the Indian tech job market, Naukri gives you breadth and CutShort gives you depth. CutShort is where Indian startups list engineering, product, and design roles, which makes it a much denser signal for tech hiring specifically than a general-purpose job board.

It also has no public API. Public search results are the only dependable surface, so extracting anything at scale means scraping.

What the data looks like

Each listing resolves to one record:

{
  "job_id": "senior-backend-engineer-bengaluru-a1b2c3",
  "title": "Senior Backend Engineer",
  "company": "Example Technologies",
  "locations": ["Bengaluru", "Remote"],
  "salary": "₹30L - ₹45L PA",
  "skills": ["Go", "PostgreSQL", "Kubernetes", "gRPC"],
  "experience_min_years": 5,
  "experience_max_years": 9,
  "remote": true,
  "url": "https://cutshort.io/job/...",
  "scraped_at": "2026-07-30T09:14:22.104Z"
}

The two fields that make this useful beyond a job board clone are skills and the experience band. Together they let you answer questions a raw listing feed cannot: which frameworks are Indian startups actually hiring for this quarter, what experience band commands what salary, and which companies are staffing up in which cities.

Why not just scrape it yourself

You can, and for a one-off pull you probably should. The reasons teams stop doing that are consistent:

• Salary strings are inconsistent. Ranges appear in lakhs per annum, sometimes per month, sometimes absent, sometimes as a single figure. Normalising this is most of the parsing work.
• Pagination changes. Search result pagination on CutShort is not stable across role categories, and a naive page-increment loop silently stops early or repeats.
• Locations are multi-valued. A single role can list several cities plus a remote flag, and flattening that to one string loses the filter you actually wanted.

Running it as an actor

The CutShort Jobs Scraper handles the above and bills per job returned, so an empty search costs nothing.

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("themineworks/cutshort-jobs-scraper").call(run_input={
    "searchQueries": ["backend engineer"],
    "maxJobs": 50,
})

for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(job["title"], job["company"], job.get("salary"), job.get("skills"))

Joining it with Naukri for a fuller picture

Neither board alone gives you the Indian tech market. A pattern that works well:

1. Pull CutShort for startup and product-company roles.
2. Pull Naukri for the same skill keywords to capture enterprise and services hiring.
3. Join on normalised company name, and treat a role appearing on both as a stronger hiring signal than one appearing on either.

That join is also how you spot companies moving from one hiring channel to the other, which tends to precede a change in hiring volume.

Use in Claude, ChatGPT & any MCP agent

Both scrapers are exposed as MCP tools, so an agent can query them directly:

https://mcp.apify.com/?tools=themineworks/cutshort-jobs-scraper

Or use the full The Mine Works MCP server for 29 data tools in one endpoint, including Naukri, IndiaMART, JustDial, and the rest of the India set.