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Animall logo — Animall MLOps Engineering Intern in Amritsar, Punjab
Animall VerifiedActively Hiring

MLOps Engineering Intern

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Location

Amritsar, Punjab

Work Mode

In Office

Duration

2 months

Stipend

₹24,000/mo

Start Date

Immediate / Rolling

Openings

9 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹24,000/moApply on Company Website ↗

About the Internship

Animall has an open MLOps Engineering Intern position in Amritsar for motivated freshers and final-year students. You will work on live MLOps and model infrastructure projects and finish with something you can genuinely talk about in interviews. About the role As a MLOps Engineering Intern at…

About Animall

Animall offers a mentor-led MLOps Engineering Intern in Amritsar with live project work in machine learning, a stipend of ₹24,000/month and a Pre-Placement Offer opportunity for strong performers.
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Amritsar, PunjabVerified employer9 openings
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Skills Required

PythonDockerMLflowCI/CDKubernetes BasicsModel Serving APIsData VersioningMonitoring & Drift DetectionAWS or GCPGit

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT/ECE), BCA/MCA or B.Sc (Computer Science / IT) who are based in or willing to work from Amritsar. You should be keen to learn Python, Docker, MLflow 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

Live project experienceExposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionals

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Online aptitude / domain assessment
  3. 3Technical interview on fundamentals and your projects
  4. 4HR discussion — stipend, start date, expectations and offer
24,000/month

Stipend · 2 months

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Openings9 positions
Applicants0 applied
ModeIn Office
CategoryMachine Learning
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