7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping

Bashiri Smith · Facebook reel · 2026-09-25 · 0:07 · 46,284 views · Open on Facebook

Topics: Start Here: Roadmaps & Strategy, LLMOps, Deployment & Monitoring, Portfolio Projects · Level: intermediate

Summary

This short reel has no speech. In the caption, the creator lists 7 habits for becoming an AI engineer: read serious AI engineering books, learn software fundamentals and product thinking, build real projects with production tools, understand how LLM systems work, read AI research every week, learn deployment, observability and evals, and ship work publicly. His main point is that people get hired for building reliable production systems, not for copying tutorials.

Key points

• Habit 1: Read real AI engineering books, such as the LLM Engineer's Handbook, Chip Huyen's work and Building Agentic AI.
• Habit 2: Master software fundamentals and product thinking. AI engineering is still software engineering.
• Habit 3: Build projects that solve real problems using production tools: LangGraph (orchestration), LiteLLM (model gateway), Langfuse (tracing), pgvector (vector storage) and Docker (packaging).
• Habit 4: Understand how LLM systems actually work: RAG, embeddings, reranking, evals and context engineering.
• Habit 5: Read AI research weekly on arXiv and Hugging Face Papers, and follow Sebastian Raschka's breakdowns.
• Habit 6: Learn deployment, observability and evals with Vercel, AWS, OpenTelemetry, Grafana and LangSmith.
• Habit 7: Ship projects publicly on GitHub and LinkedIn, with technical write-ups and architecture diagrams.
• Hiring signal: show that you can build reliable production systems, not tutorial copies.

Resources mentioned

• LLM Engineer's Handbook · book · pauliusztin.ai · paid
  A book by Paul Iusztin and Maxime Labonne on building production LLM systems from start to finish, including RAG, fine-tuning and LLMOps.
• Chip Huyen · person · huyenchip.com · free
  An author of widely read books on AI engineering and ML systems design (for example, AI Engineering and Designing Machine Learning Systems).
• Building Agentic AI · book · amazon.com · paid
  A book on designing and building agentic AI systems.
• LangGraph · tool · langchain.com · free
  Open-source library for building stateful LLM workflows as graphs, with branching, loops and human-in-the-loop pauses.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), LangChain vs. LangGraph Explained with One RAG Chatbot (Bashiri Smith on Facebook · notes), Wannabe vs $200K+ AI Engineer: RAG, Agents, Context and Fine-Tuning Mistakes (Bashiri Smith on Facebook · notes), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes) and 2 more
• LiteLLM · tool · github.com · free · recommended by both Bashiri Smith & Melvin Vivas
  An open-source LLM gateway/proxy that gives one API for many model providers, with cost tracking and pricing features.
  Also in: Using Codex to Fix a LiteLLM Pricing-Markup Bug in Docker (Melvin Vivas on X · notes), Codex Finds a Bug in a LiteLLM Feature (Melvin Vivas on X · notes)
• Langfuse · tool · langfuse.com · free
  Listed under platforms that combine tracing and evaluation.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), Taking a RAG App to Production: Evals, Guardrails, Cost and Tracing (Bashiri Smith on Facebook · notes)
• pgvector · tool · github.com · free
  An open-source Postgres extension for storing embeddings and running vector similarity search.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval (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), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), AI Engineer Roadmap for 2026 in 60 Seconds (Bashiri Smith on Facebook · notes) and 5 more
• arXiv · website · arxiv.org · free · recommended by both Bashiri Smith & Melvin Vivas
  A free archive of research preprints, with daily listings of new papers in fields like Artificial Intelligence, Machine Learning, and Computational Engineering, Finance, and Science.
  Also in: Generate Job-Worthy AI Project Ideas from Recent Research Papers + ChatGPT (Bashiri Smith on Facebook · notes), The complete arXiv corpus as a Hugging Face dataset (Melvin Vivas on X · notes), Software Engineer to AI Engineer: Job Boards, Stack, Projects and Learning Sites (Bashiri Smith on Facebook · notes), Red Flags That Hurt Your Chances of Landing an AI-Era Coding Job (Bashiri Smith on Facebook · notes)
• Hugging Face Papers · website · huggingface.co · free
  A daily, community-curated feed of trending AI research papers on Hugging Face.
• Sebastian Raschka · person · sebastianraschka.com · free
  An ML researcher and author known for clear breakdowns of LLM research and architectures (Ahead of AI newsletter, Build a Large Language Model (From Scratch)).
• Vercel · tool · x.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Deploy web apps and frontends.
  Also in: OpenAI DevDay 2026 Recap: Dots, Agents API, Codex Cloud & Marketplace (Melvin Vivas on X · notes), Jev model added to the AIBackends API via Vercel AI Gateway (Melvin Vivas on X · notes), Open models now dominate token volume on Vercel AI Gateway (Melvin Vivas on X · notes), Adding Vercel AI Gateway as a provider in AIBackends with Devin (Melvin Vivas on X · notes) and 7 more
• AWS · tool · aws.amazon.com · free
  Amazon Web Services cloud platform for deploying and scaling applications, with a free tier.
• OpenTelemetry · tool · opentelemetry.io · free
  An open-source observability standard and toolkit for collecting traces, metrics and logs.
• Grafana · tool · grafana.com · free
  An open-source dashboarding and monitoring platform for visualizing metrics, logs and traces.
• LangSmith · tool · docs.smith.langchain.com · free
  Tracing, logging and evals; also used to track cost and latency.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes)
• GitHub · tool · github.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Code hosting platform whose CI runs and pull requests the Cursor agents monitor and open.
  Also in: Grok Bot Can Now Hand Off Coding Tasks to Cursor (Melvin Vivas on X · notes), ChatGPT Can Commit Code Directly to Your GitHub Repo (Melvin Vivas on X · notes), Grok Bot Templates: Sharing, Publishing and Safely Installing Bots (Melvin Vivas on X · notes), Cursor Projects: Long-Lived Agents That Manage Fleets of Coding Agents (Melvin Vivas on X · notes) and 9 more
• LinkedIn · website · linkedin.com · free
  Professional network, used here to find warm-lead connections and the names of contacts for cold outreach.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), 6 Steps to Build a Resume Project That Lands an AI Engineering Job (Bashiri Smith on Facebook · notes), 6-Step Framework for Building AI Projects Target Companies Care About (Bashiri Smith on Facebook · notes), Beat the Hiring "Signal Problem": Referrals, Targeting, and Volume (Bashiri Smith on Facebook · notes) and 3 more
• The Complete AI Engineer Roadmap for 2026 (Exact Courses + Step-by-Step) · video · youtube.com · free
  Bashiri Smith's free YouTube training on upgrading your skills and becoming competitive for AI engineering roles, with specific courses listed step by step.
  Also in: SWE-to-AI Engineer Plan for 2027: LLMs, RAG, Agents, Evals, Job Search (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), Software Engineer to AI Engineer Before 2027: A 5-Step Career Plan (Bashiri Smith on Facebook · notes) and 19 more

Try this

• Read real AI engineering books: LLM Engineer's Handbook, Chip Huyen's books and Building Agentic AI.
• Strengthen your software fundamentals and product thinking.
• Build projects that solve real problems using LangGraph, LiteLLM, Langfuse, pgvector and Docker.
• Study how LLM systems work: RAG, embeddings, reranking, evals and context engineering.
• Read AI research every week on arXiv and Hugging Face Papers, and follow Sebastian Raschka's breakdowns.
• Learn deployment, observability and evals with Vercel, AWS, OpenTelemetry, Grafana and LangSmith.
• Ship projects publicly on GitHub and LinkedIn, with technical write-ups and architecture diagrams.
• Comment "STEPS" on the reel to get the creator's full roadmap.
• Build a production-style LLM app that solves a real problem: LangGraph orchestration, LiteLLM model gateway, pgvector retrieval, Langfuse tracing, all containerized with Docker.
• Add a RAG pipeline with reranking and evals, deploy it to Vercel or AWS, and monitor it with OpenTelemetry, Grafana or LangSmith.
• Publish the project on GitHub with an architecture diagram and a technical write-up on LinkedIn.