Location
Amritsar, Punjab
Work Mode
Hybrid
Duration
4 months
Stipend
₹27,000/mo
Start Date
Immediate / Rolling
Openings
10 positions
Type
Full-time
Category
Machine Learning
About the Internship
Join Pocketly in Amritsar as an Indic & Multilingual NLP Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on Indian-language natural language processing work.
About the role
As an Indic & Multilingual…
About Pocketly
Pocketly offers a mentor-led Indic & Multilingual NLP Intern in Amritsar with live project work in machine learning, a stipend of ₹27,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Pocketlyinternships →Amritsar, PunjabVerified employer10 openings
Apply to Pocketly →Skills Required
Natural Language Processing (NLP)Indic Language ProcessingHugging Face TransformersPythonTokenisation for Indic ScriptsTransliterationDataset CurationModel Fine-TuningEvaluation MetricsMultilingual Embeddings
Who Can Apply
- Final-year students and recent graduates from B.E./B.Tech (CSE/IT), MCA, M.Sc (CS) or M.A. (Linguistics) with technical skills who are based in or willing to work from Amritsar. You should be keen to learn Natural Language Processing (NLP), Indic Language Processing, Hugging Face Transformers 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
Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hours
Interview & Selection Process
4 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Online aptitude / domain assessment
- 3Technical interview on fundamentals and your projects
- 4HR discussion — stipend, start date, expectations and offer
₹27,000/month
Stipend · 4 months
Openings10 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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