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GoodMeetings logo — GoodMeetings MLOps Engineer Intern in Udaipur, Rajasthan
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MLOps Engineer Intern

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Location

Udaipur, Rajasthan

Work Mode

Hybrid

Duration

2 months

Stipend

₹32,000/mo

Start Date

Immediate / Rolling

Openings

4 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹32,000/moApply on Company Website ↗

About the Internship

Join GoodMeetings in Udaipur 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 GoodMeetings, your core…

About GoodMeetings

GoodMeetings offers a mentor-led MLOps Engineer Intern in Udaipur with live project work in machine learning, a stipend of ₹32,000/month and a Pre-Placement Offer opportunity for strong performers.
Udaipur, RajasthanVerified employer4 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 Udaipur. 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

Flexible working hoursNetworking with senior professionalsInternship CertificateLetter of RecommendationMentorship from a named mentor

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Technical interview on fundamentals and your projects
  3. 3Practical assignment or case study with a short walkthrough
  4. 4HR discussion — stipend, start date, expectations and offer
32,000/month

Stipend · 2 months

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Openings4 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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