Naukri is India's largest job board — bigger, for Indian hiring, than Indeed or LinkedIn Jobs. It's also a real gap in the scraping-blog ecosystem: neither Apify nor Monid has a single post about it, despite both covering Indeed, Glassdoor, and LinkedIn Jobs extensively. If you're doing India-focused recruiting or talent-market research, the usual global job APIs don't actually cover the market you're looking at.
If you're an agent (or building one) reading this rather than a human, the full machine-readable schema for every endpoint below lives at mindcase.co/skills.md.
What makes Naukri data useful for the Indian hiring market?
A few things Naukri carries that a global job board doesn't:
- CTC benchmarks in Lakhs. Naukri prices roles in Indian Cost-to-Company terms (Lakhs per annum), the unit Indian candidates and recruiters actually think in — not a currency-converted USD salary band.
- Walk-in interview details. Walk-in hiring drives — show up on a date, at a venue, no prior application — are a real, common Indian hiring practice with no equivalent on Indeed or LinkedIn Jobs.
- Campus hiring fields. Required undergraduate and postgraduate course data lets you filter for roles actually open to fresh graduates, not just keyword-match a job title.
- Work-mode and department filters built around how Indian job postings are actually categorized, rather than mapped awkwardly from a global taxonomy.
- Internships as a distinct listing type, with their own stipend bands and duration, separate from full-time postings — useful if you're tracking campus recruitment pipelines rather than lateral hiring.
Salary itself filters in ten real bands, from ₹0–3 lakh up through ₹5
crore-plus (100to500, 501) — enough resolution to separate entry-level
listings from senior/leadership roles without parsing free-text salary
strings yourself.
Why doesn't a global job API cover this?
Indeed and LinkedIn Jobs are built for a global market, and their taxonomy and filters reflect that. Salary appears in whatever the posting states, no CTC-Lakhs normalization. Walk-in drives aren't a distinct listing type outside India, so there's no field for it. Campus/fresher hiring shows up as a job title match at best, not a structured filter. None of that is a bug in those APIs — it's a market they weren't built for.
How do you pull Naukri job listings?
Say you're building a hiring-intelligence dashboard for a staffing agency that places software engineers in Bangalore.
curl -X POST "https://api.mindcase.co/v1/data/naukri/job/run?wait=true" \
-H "Authorization: Bearer $MINDCASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"params": {
"inputs": "software engineer",
"cities": ["Bangalore"],
"experience": "3",
"workMode": ["hybrid", "office"],
"maxResults": 100
}
}'
# $0.002 per job returnedEach row comes back with 50 fields — title, company, salary band, skills,
and the India-specific fields above (CTC benchmarks, walk-in details when
the listing is a walk-in drive, required UG/PG course when the role is
campus hiring). Pass fetchDetails: true and each row hydrates from the
base 50 fields to a 64-column record: full job description and
requirements text, plus the employer's AmbitionBox rating, review count,
and reviews URL alongside it — India's closest equivalent to Glassdoor,
bundled into the same call instead of a second lookup.
What can't you get from Mindcase?
Two cases.
You want a job board outside Naukri's own listings. This endpoint reads Naukri specifically. A role posted only on a company's own careers page, or only on LinkedIn, won't show up here — pair this with LinkedIn Jobs or Indeed Jobs if you need cross-platform coverage.
Some filter options are numeric category codes with no published label
list. department, roleCategory, industry, companyType, and
topCompanies all take coded values (e.g. roleCategory: 1028) rather
than plain text — the actual label mapping isn't in the documented schema,
so a code-to-name lookup takes an extra expand=options call rather than
being guessable from the field name alone.
Which endpoint should you use for which job?
| Endpoint | Input | Price | Best for |
|---|---|---|---|
| Naukri Jobs | Keyword or Naukri search URL | $0.002 / job | India-specific hiring data — CTC, walk-ins, campus roles |
| LinkedIn Jobs | Search filters | Varies | Global roles, LinkedIn-specific postings |
| Indeed Jobs | Country domain + search | Varies | Broad global job-board coverage |
How do you use this data responsibly?
Job listing data is public by nature — posted precisely so candidates find it. Still worth doing right: don't republish full listings verbatim as your own content, attribute Naukri as the source if you're building a public-facing aggregator, and don't scrape and store personal candidate data that isn't part of the listing itself (this endpoint returns job postings, not applicant records).
FAQ
No. Requests run against public job listing data — no login, no session, no cookies to manage.
Yes. The salaryRange parameter takes bands in Indian Lakhs per Annum, matching how Naukri itself prices roles.
A hiring drive where candidates show up in person on a set date at a set venue, no prior application required. It's common in Indian hiring and has no real equivalent on Indeed or LinkedIn Jobs. When a listing is a walk-in, the walkInVenue, walkInStart, walkInEnd, and walkInContact fields are populated.
Live on every run, no caching — you can also filter to only recently-posted roles with the freshness parameter (last 1, 3, 7, 15, or 30 days).