Icertis

Research Intern (AI/ML)

InternshipHybridHaldwani, Uttarakhand6 months20K/mo4 openingsImmediate / Rolling

About this internship

Icertis is hiring a Research Intern (AI/ML) in Haldwani — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to applied AI and machine learning rather than a certificate-only programme. About the role As a Research Intern (AI/ML) at Icertis, your core…

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Skills you'll use

PythonDeep LearningLiterature ReviewPyTorchExperiment DesignStatisticsPaper WritingModel EvaluationGitReproducibility

What you'll do

Work on training, evaluating and deploying models alongside the core team
Apply Python, Deep Learning, Literature Review, PyTorch 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/MCA/M.Sc (CS, Statistics, Maths) — 2026 freshers & final-year students welcome
Working knowledge of Python, Deep Learning, Literature Review (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 Haldwani, Uttarakhand

Perks & benefits

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

Interview & selection process

3 rounds — typical selection flow for this role:

1Application screening & resume shortlisting by the talent team
2Technical interview on fundamentals and your projects
3HR discussion — stipend, start date, expectations and offer

About Icertis

Icertis operates in the saas sector and has an active presence in Haldwani. 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 distance. The work runs at…

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