WinZO

AI / Machine Learning Intern

InternshipHybridDelhi, Delhi6 months25K/mo4 openingsImmediate / Rolling

About this internship

WinZO is hiring an AI / Machine Learning Intern in Delhi — 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 an AI / Machine Learning Intern at WinZO, your…

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Skills you'll use

PythonMachine LearningDeep LearningPyTorch / TensorFlowScikit-learnPandasFeature EngineeringModel EvaluationSQLMLOps Basics

What you'll do

Work on training, evaluating and deploying models alongside the core team
Apply Python, Machine Learning, Deep Learning, PyTorch / TensorFlow 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, Machine Learning, 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 Delhi, Delhi

Perks & benefits

Exposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship Certificate

Interview & selection process

3 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
3HR discussion — stipend, start date, expectations and offer

About WinZO

WinZO operates in the gaming sector and has an active presence in Delhi. 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 distance. The work runs at…

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