@DeRonin_: if i had 6 months to become an AI engineer from zero i'd do this Stage 1: the boring foundations - learn: python, git, …

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Summary

A practitioner shares a 6-month roadmap to become an AI engineer from zero, covering software foundations, LLM app development, RAG, agents and evals, deployment/reliability, and specialization — emphasizing that companies hire people who build systems on top of models, not people who train them.

if i had 6 months to become an AI engineer from zero i'd do this Stage 1: the boring foundations - learn: python, git, the terminal, http and json, basic sql, fastapi - practice: build a cli tool that calls a public api and writes clean json to disk - why: ai engineering is software engineering first, and every stage below assumes you can ship a working endpoint Stage 2: llm app development - learn: prompting, structured outputs with pydantic, tool calling, streaming, conversation state, token cost - practice: build an invoice parser that returns a typed python object, then give a model 3 tools and let it pick - why: this is the actual job, and most people skip straight past it to agents Stage 3: rag, properly - learn: embeddings, chunking, vector stores, metadata filtering, reranking - practice: ingest 20 pdfs and ship an endpoint that returns a cited answer from the top 5 chunks - why: most rag failures are retrieval failures, so whoever can debug retrieval gets paid Stage 4: agents, workflows and evals - learn: the agent loop, tool descriptions, state, retries, and when an agent is the wrong call - practice: build one agent with no framework at all, just the api, 3 tools and a while loop - why: a chain of 3 fixed calls beats an agent that might make 3 calls, and knowing which one you need is the senior part Stage 5: deployment and reliability - learn: docker, background jobs, auth, rate limits, tracing with langfuse, caching, cost control - practice: containerize your rag app with a vector db and redis, then put a hard daily spend cap on it - why: this is where most people stall, they can demo but they can't survive real traffic at 2am Stage 6: pick one lane - learn: product engineering, applied ml, or business automation - practice: ship 2-3 projects a stranger can open and use without you explaining anything - why: six months makes you dangerous in one direction, not average in three you don't need to train models you need to build systems on top of them, and that's what companies are actually paying for i wrote the full version of this with every resource and link for each stage so, if you're beginner and skipped this article 6 months ago, read below:
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if i had 6 months to become an AI engineer from zero

i’d do this

Stage 1: the boring foundations

  • learn: python, git, the terminal, http and json, basic sql, fastapi
  • practice: build a cli tool that calls a public api and writes clean json to disk
  • why: ai engineering is software engineering first, and every stage below assumes you can ship a working endpoint

Stage 2: llm app development

  • learn: prompting, structured outputs with pydantic, tool calling, streaming, conversation state, token cost
  • practice: build an invoice parser that returns a typed python object, then give a model 3 tools and let it pick
  • why: this is the actual job, and most people skip straight past it to agents

Stage 3: rag, properly

  • learn: embeddings, chunking, vector stores, metadata filtering, reranking
  • practice: ingest 20 pdfs and ship an endpoint that returns a cited answer from the top 5 chunks
  • why: most rag failures are retrieval failures, so whoever can debug retrieval gets paid

Stage 4: agents, workflows and evals

  • learn: the agent loop, tool descriptions, state, retries, and when an agent is the wrong call
  • practice: build one agent with no framework at all, just the api, 3 tools and a while loop
  • why: a chain of 3 fixed calls beats an agent that might make 3 calls, and knowing which one you need is the senior part

Stage 5: deployment and reliability

  • learn: docker, background jobs, auth, rate limits, tracing with langfuse, caching, cost control
  • practice: containerize your rag app with a vector db and redis, then put a hard daily spend cap on it
  • why: this is where most people stall, they can demo but they can’t survive real traffic at 2am

Stage 6: pick one lane

  • learn: product engineering, applied ml, or business automation
  • practice: ship 2-3 projects a stranger can open and use without you explaining anything
  • why: six months makes you dangerous in one direction, not average in three

you don’t need to train models

you need to build systems on top of them, and that’s what companies are actually paying for

i wrote the full version of this with every resource and link for each stage

so, if you’re beginner and skipped this article 6 months ago, read below:

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