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AI intern roles attract many applicants but require niche GenAI skills, increasing competition.
ML/AI technical skills are broadly transferable across industries, yielding moderate background sensitivity.
Mandatory technical skills (Python, GenAI exposure, data science basics) but no years requirement, creating moderate filter.
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Support design, development, and deployment of AI-driven solutions, especially using Generative AI models for tasks like text generation and summarization.
Apply data science techniques for data preprocessing, exploratory analysis, and model evaluation on real-world AI/ML projects.
Collaborate with engineering team members to produce clean, maintainable, and scalable Python code while researching latest AI and generative AI advancements.
Strong proficiency in Python programming and Object-Oriented Programming (OOP) concepts.
Exposure to Generative AI models (e.g., GPT, LLaMA, or similar) and understanding of prompt engineering techniques including zero-shot, one-shot, and few-shot prompting.
Basic knowledge of Data Science principles including data handling, feature engineering, and model building; familiarity with libraries like NumPy, Pandas, and Scikit-learn.
Work Experience Required: Not explicitly mentioned in the JD.
Candidate with foundational skills in Python and OOP aiming to build expertise in generative AI and real-world AI/ML application development under mentorship.
Comfortable applying data science methodologies and experimenting with AI models, showing openness to learning and problem solving.
Able to collaborate effectively in a team environment with clear communication on AI engineering tasks and innovations.