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Signzy

Computer Vision Intern

InternshipWork from HomeMumbai, Maharashtra6 months30K/mo3 openingsImmediate / Rolling

About this internship

Start your career in computer vision with Signzy as a Computer Vision Intern in Mumbai. You will sit with a working team, use the same tools and systems they do, and own a defined piece of work from day one. About the role As a Computer Vision Intern at Signzy, your core focus will be building…

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Skills you'll use

PythonComputer VisionOpenCVPyTorchCNNsObject DetectionImage SegmentationData AugmentationModel OptimisationNumPy

What you'll do

  • Work on building image and video models for detection and recognition alongside the core team
  • Apply Python, Computer Vision, OpenCV, PyTorch 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/M.Sc (CS, Electronics) — 2026 freshers & final-year students welcome
  • Working knowledge of Python, Computer Vision, OpenCV (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 Mumbai, Maharashtra

Perks & benefits

Letter of RecommendationMentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunity

Interview & selection process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Team-fit / behavioural interview with the team

About Signzy

Signzy operates in the regtech & digital onboarding sector and has an active presence in Mumbai. 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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