Apr 19
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Built from analyzing 1,700+ real AI engineering job descriptions. PHASE 1: LLM APIs (Weeks 1–3) Stop treating AI like a research project. Start building. → OpenAI API, Anthropic API, structured outputs → Prompt engineering with real evaluation frameworks → Build: a production-grade LLM wrapper with error handling, retries, and cost tracking You already know how to ship software. Now ship AI software. PHASE 2: RAG Pipelines (Weeks 4–7) This is in 60%+ of AI engineering job descriptions. → Embeddings, chunking strategies, retrieval evaluation → Tools: LlamaIndex, Pinecone, pgvector → Build: a RAG pipeline over real documents with hybrid search and reranking Not a toy chatbot. A system that actually retrieves the right context. PHASE 3: AI Agents (Weeks 8–11) Where the senior-level roles live. → Tool use, multi-step reasoning, orchestration patterns → Tools: LangGraph, CrewAI → Build: an agent system that takes action — not just generates text PHASE 4: LLMOps (Weeks 12–15) Deployment is not optional. It’s the whole point. → Prompt versioning, LLM evaluation, observability → Tools: Docker, FastAPI, LangSmith → Build: a deployed AI app with monitoring, logging, and CI/CD Recruiters don’t care what you learned. They care what you deployed. PHASE 5: Deep Learning Literacy (Weeks 16–19) You don’t need a PhD. You need enough to speak intelligently in interviews. → Neural networks, transformers architecture, fine-tuning fundamentals → Tools: PyTorch, Hugging Face → Build: a LoRA fine-tuned model on a real dataset PHASE 6: AI Infrastructure (Weeks 20–24) This is what separates $150K offers from $200K+ offers. → LLM gateways, semantic caching, model routing → Cloud deployment on AWS or GCP → Build: production infrastructure that handles real traffic 3-5 portfolio projects. 6 months. Every one of them production-grade. #ai #aiengineer #softwareengineer #tech #techjobs