About the program

We are looking for passionate and curious Machine Learning interns to help build an AIpowered Content Monitoring System for Swaralaya. The intern will work on developing intelligent pipelines capable of analyzing audio, video, text, and usergenerated content to improve platform quality and safety. This internship is ideal for students interested in AI, Machine Learning, Deep Learning, Computer Vision, Audio Processing, and realworld product development. Roles Responsibilities Assist in developing ML models for content classification and moderation Work on audiovideo analysis and metadata extraction Build systems to detect inappropriate, duplicate, or lowquality content Participate in dataset collection, preprocessing, and annotation Research and experiment with modern AIML techniques Collaborate with the product and engineering team for deployment Optimize models for performance and scalability Required Skills Basic understanding of Machine Learning concepts Familiarity with Python Knowledge of libraries such as TensorFlow, PyTorch, Scikitlearn, or OpenCV Understanding of data preprocessing and model evaluation Interest in AIdriven media or content systems Good problemsolving and analytical thinking

Perks

Real-world startup experience Opportunity to work on production-level AI systems Mentorship in ML system design and deployment Hands-on exposure to media AI and intelligent moderation systems Internship certificate and performance-based recommendation

Who can apply?

Only those candidates can apply who:

1. are from Any
2. and specialisation from
3. are available for duration of 6 Weeks
4. have relevant skills and interests

Terms of Engagement

The intern will work closely with the Swaralaya development team on real-world AI and Machine Learning tasks related to the Content Monitoring System. Interns are expected to maintain professionalism, complete assigned tasks within deadlines, participate in regular progress discussions, and follow company confidentiality guidelines regarding data and internal systems. This internship is primarily learning-oriented, and performance will be evaluated based on contribution, consistency, innovation, and technical growth throughout the internship period.

Number of openings

1