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InCred logo — InCred NLP Engineering Intern in Ghaziabad, Uttar Pradesh
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NLP Engineering Intern

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Location

Ghaziabad, Uttar Pradesh

Work Mode

Hybrid

Duration

3 months

Stipend

₹26,000/mo

Start Date

Immediate / Rolling

Openings

11 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹26,000/moApply on Company Website ↗

About the Internship

InCred is hiring a NLP Engineering Intern in Ghaziabad — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to natural language processing rather than a certificate-only programme. About the role As a NLP Engineering Intern at InCred, your core focus will…

About InCred

InCred offers a mentor-led NLP Engineering Intern in Ghaziabad with live project work in machine learning, a stipend of ₹26,000/month and a Pre-Placement Offer opportunity for strong performers.
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Ghaziabad, Uttar PradeshVerified employer11 openings
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Skills Required

PythonHugging Face TransformersPyTorchTokenisationText ClassificationNamed Entity RecognitionEmbeddingsModel EvaluationPandasGit

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (any branch), B.Sc/M.Sc (Statistics, Mathematics, Economics), BCA/MCA or MBA who are based in or willing to work from Ghaziabad. You should be keen to learn Python, Hugging Face Transformers, PyTorch on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.

Perks

Internship CertificateLetter of RecommendationMentorship from a named mentorLive project experienceExposure to professional tools and workflows

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Team-fit / behavioural interview with the team
  3. 3Online aptitude / domain assessment
  4. 4HR discussion — stipend, start date, expectations and offer
26,000/month

Stipend · 3 months

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Openings11 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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