6-Month AI Engineer Roadmap

Month 1: Get solid enough in coding and fundamentals - Python really well - Git + GitHub - CLI / terminal basics - JSON, APIs, HTTP, async basics - basic SQL - basic data handling with pandas - virtual environments, package management, error handling - FastAPI or Flask Month 2: Master LLM app development - prompting fundamentals - system vs user instructions - structured outputs / JSON schemas - function/tool calling - streaming responses - conversation state - cost / latency / token basics - failure handling - prompt injection awareness Month 3: Learn RAG properly - embeddings - chunking - vector databases - metadata filtering - reranking - retrieval quality issues - hallucination reduction - citations and grounding Month 4: Agents, tools, workflows, evals - agent loops - tool selection - state management - retries - when NOT to use agents - multi-step workflows - evaluation harnesses - task success metrics Month 5: Deployment, product thinking, and reliability - FastAPI production patterns - Docker - background jobs - queues - auth + API key security - logging - observability - prompt/version management - eval dashboards - cost monitoring - rate limits - caching Month 6: Specialize and become hireable Choose one direction and focus on practice: Direction 1: AI product engineer (LLM apps, RAG, agents, deployment, product UX) Direction 2: Applied ML / LLM engineer (fine-tuning, when to fine-tune vs prompt, evaluation, inference optimization, open-source models, training pipelines) Direction 3: AI automation engineer (workflow orchestration, business process automation, multi-tool systems, CRM, docs, email, support, ops use cases)