AI-Generalist || Trainee || Premium Institute || Stipend

2 weeks ago


Noida, India smc insurance Full time

About Us

We ,SMC Insurance Brokers Pvt Ltd are a fast-growing organization committed to leveraging cutting-edge technologies to deliver innovative solutions in the insurance domain. As part of our vision, we are building AI-driven applications that transform the way businesses and customers interact.

Website:

Role Overview

We are looking for enthusiastic B.Tech students from premium institutes who are passionate about Artificial Intelligence and eager to explore real-world applications of AI development. As an intern, you will work with our experienced tech team on challenging projects, gain hands-on exposure, and contribute to building intelligent solutions.

Key Responsibilities

  • Assist in designing, developing, and testing AI/ML models for real-world applications.
  • Support data preprocessing, feature engineering, and exploratory analysis.
  • Collaborate with cross-functional teams (Tech, Product, HR) to integrate AI solutions.
  • Conduct literature reviews and stay updated on the latest AI trends and frameworks.
  • Document development processes, findings, and results.

Eligibility & Skills Required

Education: B.Tech (Computer Science, IT, AI/ML, Data Science, or related fields) from premium institutes.

Internship Period- 3 to 6 months

Strong foundation in Python, algorithms, and data structures.

Knowledge of AI/ML frameworks such as Tensor Flow, PyTorch, Scikit-learn, etc.

Familiarity with NLP, computer vision, or generative AI concepts will be an advantage.

Strong problem-solving and analytical skills.

Ability to work independently as well as in a collaborative team environment.

What We Offer

Hands-on experience in real-world AI development projects.

Mentorship from senior professionals and domain experts.

Opportunity to contribute to impactful business solutions.

Stipend as per industry standards.

Exposure to the insurance-tech ecosystem.

Pre-placement offer will be made to the trainees based on the performances



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