Stoxkart

MLOps Engineer Intern

InternshipHybridSurat, Gujarat2 months44K/mo4 openingsImmediate / Rolling

About this internship

Stoxkart is hiring a MLOps Engineer Intern in Surat — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to ML platform and operations rather than a certificate-only programme. About the role As a MLOps Engineer Intern at Stoxkart, 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 Surat, Gujarat

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
2Technical interview on fundamentals and your projects

About Stoxkart

Stoxkart operates in the stock broking sector and has an active presence in Surat. 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…

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