Step-by-Step Roadmap to a $200K+ AI Engineering Role

Bashiri Smith · Facebook reel · 2026-10-01 · 0:49 · 410 views · Open on Facebook

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

Summary

This is a quick roadmap for landing a well-paid AI engineering job. First, learn a core stack: Python, FastAPI, the LangChain family of tools, Langfuse, Terraform and vector databases. Add machine learning basics and a working understanding of how LLMs work, and build projects with those skills. Then use AI to compare your skills against what target companies are hiring for, build a project that fills the gap, post about it on LinkedIn, and prepare for interviews with a focus on system design and how you use AI tools.

Key points

• Core stack: Python, FastAPI, LangChain, Langfuse, LangSmith, LangGraph and Terraform.
• Vector databases to learn: pgvector, Weaviate and Pinecone.
• Machine learning basics: PyTorch, scikit-learn and a basic understanding of how LLMs work.
• Build projects with each skill you learn.
• Research your target companies: what teams they have and what roles they're hiring for. Give that to an AI and ask for a market gap analysis that shows which skills you're missing and what you should build.
• Build the project the gap analysis points to, add it to your resume (AI can write the entry for you), and post about it on LinkedIn.
• In interview practice, spend much more time on system design and how you use AI tools than on hand-coding.
• Once you get an interview, ask what specific problem the company wants this hire to solve.

Resources mentioned

• 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), Software Engineer to AI Engineer Before 2027: A 5-Step Career Plan (Bashiri Smith on Facebook · notes), 6 Free Videos to Move from Software Engineer to AI Engineer (Bashiri Smith on Facebook · notes) and 19 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), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes), 5 Things AI Engineers Must Evaluate: RAG, Agents, Models, Data, Guardrails (Bashiri Smith on Facebook · notes) and 76 more
• Python · tool · python.org · free · recommended by both Bashiri Smith & Melvin Vivas
  A programming language that comes preinstalled on Droplets created by the plugin.
  Also in: Software Engineer to AI Engineer: Job Boards, Stack, Projects and Learning Sites (Bashiri Smith on Facebook · notes), Run Codex in the Cloud: Spin Up DigitalOcean Droplets with the Codex Plugin (Melvin Vivas on X · notes)
• FastAPI · tool · fastapi.tiangolo.com · free
  Wrap your models as real inference APIs.
  Also in: 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 for 2026 in 60 Seconds (Bashiri Smith on Facebook · notes), AI Engineer Roadmap: Fundamentals, RAG, Agents, Books & Your First AI Service (Bashiri Smith on Facebook · notes)
• LangChain · tool · github.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Open-source framework with ready-made integrations and common interfaces for connecting LLMs, embedding models, vector stores and tools.
  Also in: SWE-to-AI Engineer Plan for 2027: LLMs, RAG, Agents, Evals, Job Search (Bashiri Smith on Facebook · notes), Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), LangChain vs. LangGraph Explained with One RAG Chatbot (Bashiri Smith on Facebook · notes), How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes) and 3 more
• Langfuse · tool · langfuse.com · free
  Listed under platforms that combine tracing and evaluation.
  Also in: 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes), Taking a RAG App to Production: Evals, Guardrails, Cost and Tracing (Bashiri Smith on Facebook · notes)
• LangSmith · tool · docs.smith.langchain.com · free
  Tracing, logging and evals; also used to track cost and latency.
  Also in: 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes)
• 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: 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), 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 2 more
• Terraform · tool · terraform.io · free · recommended by both Bashiri Smith & Melvin Vivas
  Infrastructure-as-code tool for creating and managing cloud infrastructure.
  Also in: Cautionary Tale: Claude Code Wiped a Production Database via Terraform (Melvin Vivas on X · notes)
• pgvector · tool · github.com · free
  An open-source Postgres extension for storing embeddings and running vector similarity search.
  Also in: How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes), How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval (Bashiri Smith on Facebook · notes)
• Weaviate · tool · weaviate.io · free
  An open-source vector database for storing embeddings and running semantic and hybrid search.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (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), Why RAG Alone Won't Make You a Lasting AI Engineer (Bashiri Smith on Facebook · notes)
• Pinecone · tool · pinecone.io · free
  A managed, production vector database (free tier).
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (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), Why RAG Alone Won't Make You a Lasting AI Engineer (Bashiri Smith on Facebook · notes)
• PyTorch · tool · pytorch.org · free · recommended by both Bashiri Smith & Melvin Vivas
  Deep-learning framework. Its built-in scaled dot-product attention (SDP) was the baseline in this benchmark.
  Also in: Wannabe vs $200K+ AI Engineer: RAG, Agents, Context and Fine-Tuning Mistakes (Bashiri Smith on Facebook · notes), AI DevBox v1.3.0: a GPU-ready Docker image with coding-agent CLIs (Melvin Vivas on X · notes), Docker image with coding agents pre-installed on a CUDA + PyTorch base (Melvin Vivas on X · notes), Software Engineer to AI Engineer: Job Boards, Stack, Projects and Learning Sites (Bashiri Smith on Facebook · notes) and 5 more
• scikit-learn · tool · scikit-learn.org · free
  A Python machine learning library for building pipelines and training classic ML models.
  Also in: Five Core AI Engineering Topic Areas: A Study Roadmap Checklist (Bashiri Smith on Facebook · notes)
• LinkedIn · website · linkedin.com · free
  Professional network, used here to find warm-lead connections and the names of contacts for cold outreach.
  Also in: 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (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

Try this

• Learn Python, FastAPI, LangChain, Langfuse, LangSmith, LangGraph, Terraform and vector databases (pgvector, Weaviate, Pinecone).
• Learn machine learning basics with PyTorch and scikit-learn, and get a basic understanding of how LLMs work.
• Build projects using each of these skills.
• Research your target companies' teams and the roles they're hiring for.
• Give that research to an AI and ask for a market gap analysis of the skills you're missing and what you should build.
• Build the project the gap analysis suggests and add it to your resume (you can ask AI to write the entry).
• Post on LinkedIn about the project.
• Practice interviewing, with a focus on system design and how you use AI tools.
• In interviews, ask what specific problem the company wants this hire to solve.
• Watch the full roadmap video.
• A portfolio project chosen from an AI-assisted market gap analysis of what your target companies are hiring for.