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Toffee Insurance logo — Toffee Insurance Deep Learning Intern in Bhopal, Madhya Pradesh
Toffee Insurance VerifiedActively Hiring

Deep Learning Intern

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Location

Bhopal, Madhya Pradesh

Work Mode

Hybrid

Duration

3 months

Stipend

₹23,000/mo

Start Date

Immediate / Rolling

Openings

9 positions

Type

Full-time

Category

Machine Learning

Apply Now — ₹23,000/moApply on Company Website ↗

About the Internship

Toffee Insurance is hiring a Deep Learning Intern in Bhopal — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to deep learning and neural networks rather than a certificate-only programme. About the role As a Deep Learning Intern at Toffee Insurance…

About Toffee Insurance

Toffee Insurance offers a mentor-led Deep Learning Intern in Bhopal with live project work in machine learning, a stipend of ₹23,000/month and a Pre-Placement Offer opportunity for strong performers.
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Bhopal, Madhya PradeshVerified employer9 openings
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Skills Required

Deep LearningPyTorchNeural Network ArchitecturesPythonGPU TrainingBackpropagation & OptimisersRegularisationTransfer LearningExperiment TrackingError Analysis

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/ECE/AI-ML), M.Tech, MCA or M.Sc (CS / Mathematics) who are based in or willing to work from Bhopal. You should be keen to learn Deep Learning, PyTorch, Neural Network Architectures 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. 2Discussion with the hiring manager on the role and your fit
23,000/month

Stipend · 3 months

Apply Now →
Openings9 positions
Applicants1 applied
ModeHybrid
CategoryMachine Learning
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