In September 2026 I ran a series of LinkedIn posts that delivered hands‑on resources, interview‑centric guides, and tooling demos for AI engineers. Each post contained a short hook, a concrete deliverable (e.g., a tutorial or project list), and a shortened URL to the full content.
I shared 25 free resources that take learners from AI fundamentals to LLMs, RAG & AI agents.
I listed 20 AI‑agent project ideas worth building.
I highlighted 5 AI tools that are becoming essential for cracking AI Engineer roles.
I proposed a 30‑day interview preparation plan for AI Engineer candidates.
These posts emphasized hands‑on tooling (CI/CD pipelines, Docker, GitHub Actions) to ensure production‑ready LLM apps, and interview‑centric content to meet market demand for hiring‑ready talent. I also included real‑world project lists (AI agents, RAG, monitoring dashboards) to bridge theory‑practice gaps.
- Multi‑agent market research with CrewAI (09‑30)
- Live ML coding interviews beyond pure coding (09‑29)
- CI/CD pipeline for LLM apps using tests, evals, Docker & GitHub Actions (09‑27)
- Shifts in company expectations for AI engineers (09‑26)
- Tutorial: build a Python AI coding agent that writes, tests, and improves code (09‑23)
- Framework to confidently explain AI projects in interviews (09‑22)
- Five AI tools essential for AI Engineer roles (09‑21)
- Guide to build an ML model‑monitoring dashboard in Python (09‑20)
- Overview of evolving AI Engineer interview formats (09‑19)
- RAG project ideas worth building (09‑18)
- List of companies hiring remote AI/ML roles (09‑17)
- LLM evaluation dashboard tutorial in Python (09‑16)
- Automated pipeline that detects model performance drops and retrains (09‑13)