eInfochips

Computer Vision Intern

InternshipHybridVaranasi, Uttar Pradesh4 months22K/mo1 openingImmediate / Rolling

About this internship

eInfochips is hiring a Computer Vision Intern in Varanasi — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to applied AI and machine learning rather than a certificate-only programme. About the role As a Computer Vision Intern at eInfochips, your core…

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Skills you'll use

OpenCVPythonDeep LearningImage ProcessingPyTorchDataset AnnotationModel EvaluationNumPyGitCUDA Basics

What you'll do

Work on training, evaluating and deploying models alongside the core team
Apply OpenCV, Python, Deep Learning, Image Processing 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 OpenCV, Python, Deep Learning (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 Varanasi, Uttar Pradesh

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
2Online aptitude / domain assessment

About eInfochips

eInfochips operates in the product engineering sector and has an active presence in Varanasi. 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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