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Thomas Cook India logo — Thomas Cook India MLOps Engineer Intern in New Delhi, Delhi
Thomas Cook India VerifiedActively Hiring

MLOps Engineer Intern

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Location

New Delhi, Delhi

Work Mode

Hybrid

Duration

3 months

Stipend

₹46,000/mo

Start Date

Immediate / Rolling

Openings

2 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹46,000/moApply on Company Website ↗

About the Internship

Thomas Cook India is hiring a MLOps Engineer Intern in New Delhi — 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 Thomas Cook India…

About Thomas Cook India

Thomas Cook India offers a mentor-led MLOps Engineer Intern in New Delhi with live project work in machine learning, a stipend of ₹46,000/month and a Pre-Placement Offer opportunity for strong performers.
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New Delhi, DelhiVerified employer2 openings
Apply to Thomas Cook India

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 New Delhi. 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

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

Interview & Selection Process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Technical interview on fundamentals and your projects
46,000/month

Stipend · 3 months

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Openings2 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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