AI Engineer Roadmap for 2026 in 60 Seconds

Bashiri Smith · Facebook reel · 2026-09-11 · 0:55 · 19,277 views · Open on Facebook

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

Summary

Bashiri Smith gives a quick five-stage roadmap for becoming an AI engineer in 2026. Stage 1 is software engineering basics (Python, Git, SQL, APIs). Stage 2 is learning how models work with Hugging Face's LLM course, followed by building RAG and agent projects. The last stages are shipping to production with FastAPI and Docker, adding evaluations and monitoring, preparing for interviews (fast app building and AI system design), and then applying with targeted outreach.

Key points

• Step 1 – Software engineering basics: learn Python, then Git, SQL and how APIs work.
• Step 2 – Understand models: use Hugging Face's LLM course to learn tokenization, transformers and inference.
• Step 3 – Build RAG and agent projects. He points to a GitHub repo with beginner-to-advanced projects but doesn't name it.
• Step 4 – Production: deploy projects with FastAPI and Docker, and add evaluations, logging and failure handling.
• Measure three things for every AI app: whether the answers are correct, how long they take (latency) and what they cost.
• Step 5 – Interview prep: practice building AI apps as fast as possible and practice AI system design.
• Be ready to explain how you chose your retrieval strategy, how you test your agent and what happens when something fails.
• Apply to relevant roles and reach out to hiring managers, recruiters and warm connections, leading with problems you've solved.

Resources mentioned

• Hugging Face LLM Course · course · huggingface.co · free
  Hands-on course on transformers, tokenizers and fine-tuning.
  Also in: How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes)
• Git · tool · git-scm.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Version control system. Conductor uses its worktree feature to give each agent an isolated copy of the repo.
  Also in: Conductor Walkthrough: Running Parallel Coding Agents in Isolated Git Worktrees (Melvin Vivas on X · notes)
• FastAPI · tool · fastapi.tiangolo.com · free
  Wrap your models as real inference APIs.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), Software Engineer to AI Engineer: Job Boards, Stack, Projects and Learning Sites (Bashiri Smith on Facebook · notes), AI Engineer Roadmap: Fundamentals, RAG, Agents, Books & Your First AI Service (Bashiri Smith on Facebook · notes)
• Docker · tool · docs.docker.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Containerize everything you ship.
  Also in: Deploying AI workflow integrations with Apache Camel in Docker (Melvin Vivas on X · notes), Devin's cloud Ubuntu sandbox ships with Docker pre-installed (Melvin Vivas on X · notes), 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes) and 5 more
• 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

• Learn Python, then Git, SQL and how APIs work.
• Take Hugging Face's LLM course to learn tokenization, transformers and inference.
• Build RAG and agent projects, going from beginner to advanced.
• Deploy your projects with FastAPI and Docker.
• Add evaluations, logging and failure handling to your projects.
• Track how correct, how fast and how expensive your app's answers are.
• Practice building AI apps quickly and practice AI system design.
• Prepare to explain your retrieval strategy, how you test your agent and how you handle failures.
• Apply to relevant roles and reach out to hiring managers, recruiters and warm connections with problems you've solved.
• A RAG app deployed with FastAPI and Docker, with evaluations, logging and failure handling, that tracks answer accuracy, latency and cost.
• An AI agent project with a test suite and failure handling.