AI Engineering Skills Map: The 5 Core Categories to Master

Bashiri Smith · Facebook reel · 2026-08-22 · 1:06 · 12,398 views · Open on Facebook

Topics: Start Here: Roadmaps & Strategy, Resume, Job Search & Interviews · Level: beginner

Summary

The creator talks about an AI engineering skills map published by an unnamed "top AI expert." That map splits the field into six areas: LLM foundations, grounding models with data, building agentic systems, evaluation-driven development, operating in production, and ML foundations. He maps these onto his own five categories: LLMs & RAG; Agents, Integration & Orchestration; Ops & Evaluation; Safety & Ethics; and ML Foundations. He argues that safety and ethics is the one area the map leaves out, and that it's needed to run AI in production. Most of the video is a pitch for his Skool community.

Key points

• The expert's skills map has 6 areas: LLM foundations, grounding models with data (RAG), building agentic systems, evaluation-driven development, operating AI in production, and machine learning foundations.
• An earlier article by the same expert described 'four pillars of AI engineering'. The new map goes into more detail on skills.
• The creator's 5 categories: (1) LLMs & RAG, (2) Agents, Integration & Orchestration, (3) Ops & Evaluation, (4) Safety & Ethics, (5) Machine Learning Foundations.
• How they map: 'LLM foundations' plus 'grounding with data' become LLMs & RAG. 'Agentic systems' becomes Agents/Orchestration. 'Eval-driven development' plus 'production' become Ops & Evaluation.
• Safety & Ethics is the only category the map doesn't name. The creator says you need it to run AI in production and to reach top-tier roles.
• Main message: you don't have to learn every AI topic online. Focus on these five areas to build real systems and get ready for interviews.

Resources mentioned

• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Use the five categories to plan your study: LLMs & RAG, Agents/Integration/Orchestration, Ops & Evaluation, Safety & Ethics, ML Foundations.
• Don't try to learn every trending AI topic. Stick to the core categories.
• Learn AI safety and ethics as part of running AI systems in production.
• Comment 'master' on the reel to get the community link (the creator's call to action).