Infosys

NLP / Generative AI Intern

InternshipIn OfficeBengaluru, Karnataka3 months24K/mo2 openingsImmediate / Rolling

About this internship

Infosys has an open NLP / Generative AI Intern position in Bengaluru for motivated freshers and final-year students. You will work on live applied AI and machine learning projects and finish with something you can genuinely talk about in interviews. About the role As a NLP / Generative AI Intern…

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Skills you'll use

PythonNLPTransformersHugging FaceText PreprocessingPyTorchModel EvaluationTokenisationGitEmbeddings

What you'll do

Work on training, evaluating and deploying models alongside the core team
Apply Python, NLP, Transformers, Hugging Face 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/M.Tech/MCA/M.Sc (CS, Statistics, Maths) — 2026 freshers & final-year students welcome
Working knowledge of Python, NLP, Transformers (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 Bengaluru, Karnataka

Perks & benefits

Live project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionals

Interview & selection process

2 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Online aptitude / domain assessment

About Infosys

Infosys operates in the it services & consulting sector and has an active presence in Bengaluru. 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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