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Siemens Healthineers logo — Siemens Healthineers MLOps Engineer Intern in Mangalore, Karnataka
Siemens Healthineers VerifiedActively Hiring

MLOps Engineer Intern

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Location

Mangalore, Karnataka

Work Mode

Hybrid

Duration

6 months

Stipend

₹37,000/mo

Start Date

Immediate / Rolling

Openings

3 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹37,000/moApply on Company Website ↗

About the Internship

Join Siemens Healthineers in Mangalore as a MLOps Engineer Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on ML platform and operations work. About the role As a MLOps Engineer Intern at Siemens…

About Siemens Healthineers

Siemens Healthineers offers a mentor-led MLOps Engineer Intern in Mangalore with live project work in machine learning, a stipend of ₹37,000/month and a Pre-Placement Offer opportunity for strong performers.
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Mangalore, KarnatakaVerified employer3 openings
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Skills Required

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

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT), MCA or M.Sc (Computer Science / Data Science) who are based in or willing to work from Mangalore. You should be keen to learn Kubeflow, MLflow, CI/CD for AI on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.

Perks

Mentorship from a named mentorLive project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hours

Interview & Selection Process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Discussion with the hiring manager on the role and your fit
37,000/month

Stipend · 6 months

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Openings3 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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