Quantiphi

Generative AI Intern

InternshipIn OfficeDehradun, Uttarakhand3 months44K/mo3 openingsImmediate / Rolling

About this internship

Quantiphi is hiring a Generative AI Intern in Dehradun — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to generative AI rather than a certificate-only programme. About the role As a Generative AI Intern at Quantiphi, your core focus will be building…

Skills you'll use

PythonLarge Language ModelsPrompt EngineeringLangChainHugging Face TransformersRAG PipelinesVector DatabasesPyTorchREST APIsModel Evaluation

What you'll do

Work on building, prompting and evaluating LLM-powered features alongside the core team
Apply Python, Large Language Models, Prompt Engineering, LangChain to live, production-facing problems
Document your work clearly for reviewers and stakeholders
Participate in reviews and act on the feedback you receive
Collaborate across engineering, product, design or business as required
Follow the team's quality, testing and documentation standards

Who can apply

Pursuing or recently completed B.E./B.Tech/BCA/MCA/M.Sc (CS, AI, Data Science) — 2026 freshers & final-year students welcome
Working knowledge of Python, Large Language Models, Prompt Engineering (or a genuine willingness to learn them)
Strong fundamentals in your discipline and clear problem-solving ability
Care with detail and the discipline to document your work
Clear written and verbal communication in English
Based in or willing to work from Dehradun, Uttarakhand

Perks & benefits

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

Interview & selection process

2 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Discussion with the hiring manager on the role and your fit

About Quantiphi

Quantiphi operates in the artificial intelligence sector and has an active presence in Dehradun. Interns here are put on real work from the start, because the team's view is that the fastest way to learn a craft is to practise it under people who already do it well — not to watch it from a…

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