Zopper

MLOps Engineer Intern

InternshipWork from HomeAgra, Uttar Pradesh6 months36K/mo4 openingsImmediate / Rolling

About this internship

Start your career in ML platform and operations with Zopper as a MLOps Engineer Intern in Agra. 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 MLOps Engineer Intern at Zopper, your core focus will be…

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Skills you'll use

KubeflowMLflowCI/CD for AIDocker / KubernetesModel Monitoring & Drift DetectionPythonFeature StoresModel Registry & VersioningCloud ML PlatformsInfrastructure as Code

What you'll do

Work on building the pipelines that get models retrained, deployed and watched alongside the core team
Apply Kubeflow, MLflow, CI/CD for AI, Docker / Kubernetes 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 (CSE/IT), MCA or M.Sc (Computer Science / Data Science) — 2026 freshers & final-year students welcome
Working knowledge of Kubeflow, MLflow, CI/CD for AI (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 Agra, Uttar Pradesh

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:

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

About Zopper

Zopper operates in the insurtech sector and has an active presence in Agra. 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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