•1 min read•from Data Science
What is expected from new grad AI engineers?
Our take
As a statistics and data science student aspiring to become an AI engineer, you’re on an exciting path. New grad AI engineers are generally expected to blend theoretical knowledge with practical skills. While proficiency in deep learning, LLM fine-tuning, and relevant tools is essential, having a solid understanding of design patterns, software architecture, and operating systems can significantly enhance your capabilities. Moreover, while familiarity with RAG components is valuable, traditional system design principles will further prepare you for the complexities of AI engineering roles.
I’m a stats/ds student aiming to become an AI engineer after graduation. I’ve been doing projects: deep learning, LLM fine-tuning, langgraph agents with tools, and RAG systems. My work is in Python, with a couple of projects written in modular code deployed via Docker and FastAPI on huggingface spaces.
But not being a CS student i am not sure what i am missing:
- Do i have to know design patterns/gang of 4? I know oop though
- What do i have to know of software architectures?
- What do i need to know of operating systems?
- And what about system design? Is knowing the RAG components and how agents work enough or do i need traditional system design?
I mean in general what am i expected to know for AI eng new grad roles?
Also i have a couple of DS internships.
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