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Kissht logo — Kissht LLM Fine-Tuning Intern in Gurugram, Haryana
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LLM Fine-Tuning Intern

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Location

Gurugram, Haryana

Work Mode

Hybrid

Duration

3 months

Stipend

₹22,000/mo

Start Date

Immediate / Rolling

Openings

7 positions

Type

Full-time

Category

Generative AI

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About the Internship

Join Kissht in Gurugram as a LLM Fine-Tuning Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on model fine-tuning and adaptation work. About the role As a LLM Fine-Tuning Intern at Kissht, your core…

About Kissht

Kissht offers a mentor-led LLM Fine-Tuning Intern in Gurugram with live project work in generative ai, a stipend of ₹22,000/month and a Pre-Placement Offer opportunity for strong performers.
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Gurugram, HaryanaVerified employer7 openings
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Skills Required

Fine-TuningLoRA / PEFTPyTorchHugging FaceDataset CurationEvaluation BenchmarksGPU TrainingPythonOverfitting ControlExperiment Tracking

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT/ECE), MCA or M.Sc (Computer Science / Statistics / Mathematics) who are based in or willing to work from Gurugram. You should be keen to learn Fine-Tuning, LoRA / PEFT, 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

Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursBroad ownership and direct exposure to founders and senior decision-makers

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
22,000/month

Stipend · 3 months

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Openings7 positions
Applicants0 applied
ModeHybrid
CategoryGenerative AI
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