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Observe.AI

Machine Learning Intern

InternshipIn OfficeBhopal, Madhya Pradesh3 months44K/mo2 openingsImmediate / Rolling

About this internship

Observe.AI has an open Machine Learning Intern position in Bhopal for motivated freshers and final-year students. You will work on live applied machine learning projects and finish with something you can genuinely talk about in interviews. About the role As a Machine Learning Intern at Observe.AI…

Skills you'll use

PythonMachine LearningScikit-learnTensorFlowPyTorchFeature EngineeringModel EvaluationPandasNumPyMLOps Basics

What you'll do

  • Work on training, tuning and validating ML models on real data alongside the core team
  • Apply Python, Machine Learning, Scikit-learn, TensorFlow 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/M.Tech/M.Sc (CS, Statistics, Maths) — 2026 freshers & final-year students welcome
  • Working knowledge of Python, Machine Learning, Scikit-learn (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 Bhopal, Madhya Pradesh

Perks & benefits

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

Interview & selection process

2 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Online aptitude / domain assessment

About Observe.AI

Observe.AI operates in the artificial intelligence sector and has an active presence in Bhopal. 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…
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