300+ AI/ML interview Q&A repo
What happened
A free GitHub with 300+ AI/ML interview Q&As covering RAG, agents, LoRA/RLHF, and LLMOps started circulating among hiring candidates this weekend. The repo is being shared as a targeted prep resource for ML/LLM engineering interview pipelines. (x.com)
Why it matters
The GitHub repository ai-interview-codex, published under the user girijesh-ai, shows 23 stars, 9 forks and 53 commits on its main branch as of the latest repository view. (github.com) The repo’s top-level folders include agentic-ai, gen-ai, system-design, ml and dsa, and it contains index files named INTERVIEW-PREP-COMPLETE-INDEX.md and MASTER-STUDY-SCHEDULE.md that map the collection. (github.com) Practical how‑tos appear in named guides such as production-rag-systems-guide.md and a notebook lora-qlora-finetuning-guide.ipynb that documents LoRA/QLoRA fine‑tuning steps. (github.com 1) (github.com 2) The agentic-ai folder contains interview-focused primers including mcp-interview-preparation-guide.md and agent-memory-architecture-guide.md that walk through multi‑agent patterns and memory designs. (github.com) The repository bundles operational interview assets—ML-CODING-INTERVIEW-MASTER-GUIDE.md, mlops-production-ml-guide.md, technical-cheatsheet.md and a leadership-stories-template.md for STAR-format behavioral answers. (github.com) (github.com) At least one public fork (ivesh/ai-interview) mirrors the main structure and retains notebooks and guides such as the LLM-ML-SYSTEM-DESIGN-MASTER-GUIDE and lora-qlora-finetuning materials. (github.com)
Key numbers
- A free GitHub with 300+ AI/ML interview Q&As covering RAG, agents, LoRA/RLHF, and LLMOps started circulating among hiring candidates this weekend.
- (x.com) The GitHub repository ai-interview-codex, published under the user girijesh-ai, shows 23 stars, 9 forks and 53 commits on its main branch as of the latest repository view.
- (github.com 1) (github.com 2) The agentic-ai folder contains interview-focused primers including mcp-interview-preparation-guide.md and agent-memory-architecture-guide.md that walk through multi‑agent patterns and memory designs.
Quick answers
What happened in 300+ AI/ML interview Q&A repo?
A free GitHub with 300+ AI/ML interview Q&As covering RAG, agents, LoRA/RLHF, and LLMOps started circulating among hiring candidates this weekend. The repo is being shared as a targeted prep resource for ML/LLM engineering interview pipelines. (x.com)
Why does 300+ AI/ML interview Q&A repo matter?
The GitHub repository ai-interview-codex, published under the user girijesh-ai, shows 23 stars, 9 forks and 53 commits on its main branch as of the latest repository view. (github.com) The repo’s top-level folders include agentic-ai, gen-ai, system-design, ml and dsa, and it contains index files named INTERVIEW-PREP-COMPLETE-INDEX.md and MASTER-STUDY-SCHEDULE.md that map the collection. (github.com) Practical how‑tos appear in named guides such as production-rag-systems-guide.md and a notebook lora-qlora-finetuning-guide.ipynb that documents LoRA/QLoRA fine‑tuning steps. (github.com 1) (github.com 2) The agentic-ai folder contains interview-focused primers including mcp-interview-preparation-guide.md and agent-memory-architecture-guide.md that walk through multi‑agent patterns and memory designs. (github.com) The repository bundles operational interview assets—ML-CODING-INTERVIEW-MASTER-GUIDE.md, mlops-production-ml-guide.md, technical-cheatsheet.md and a leadership-stories-template.md for STAR-format behavioral answers. (github.com) (github.com) At least one public fork (ivesh/ai-interview) mirrors the main structure and retains notebooks and guides such as the LLM-ML-SYSTEM-DESIGN-MASTER-GUIDE and lora-qlora-finetuning materials. (github.com)
