@_avichawla: The ultimate Full-stack AI Engineering roadmap to go from 0 to 100. Bookmark this. This is the exact mapped-out path on…

The ultimate Full-stack AI Engineering roadmap to go from 0 to 100. Bookmark this. This is the exact mapped-out path on what it actually takes to go from Beginner → full-stack AI engineer. > Start with coding fundamentals. > Learn Python, Bash, Git, and testing. > Every strong AI engineer starts with fundamentals. > Learn how to interact with models by understanding LLM APIs. > This will teach you structured outputs, caching, system prompts, etc. > APIs are great, but raw LLMs still need the latest info to be effective. > Learn how LLMs are usually augmented with more info/patterns. > This will teach you the basics of fine-tuning, RAG, prompt/context engineering, etc. > Strong LLMs are useless without context. That’s where Retrieval techniques help. > Learn about vector DBs, hybrid retrieval, indexing strategies, etc. > Once retrieval is solid, move into RAG. > Learn to build retrieval + generation pipelines, reranking, and multi-step retrieval using popular orchestration frameworks. > Now, step into AI Agents, where AI moves from answering to acting. > Learn memory, multi-agent systems, human-in-the-loop design, Agentic patterns, etc. > Learn how to ship in production with Infrastructure. > This will teach you CI/CD, containers, model routing, Kubernetes, and deployment at scale. > Focus on observability & evaluation. > Learn how to create eval datasets, LLM-as-a-judge, tracing, instrumentation, and continuous evaluation pipelines. > Security is crucial. > Learn how to implement guardrails, sandboxing, prompt injection defenses, and ethical guidelines. > Finally, explore advanced workflows. > This covers voice & vision agents, CLI agents, robotics, agent swarms, and self-refining AI systems. This is the actual journey to becoming a full-stack AI Engineer and not just "use” AI, but designing full-stack AI systems that can survive in production. If you need specific resources, I wrote a detailed article that provides a structured learning roadmap for AI engineers in 2026. It covers prompting, RAG, fine-tuning, agents, MCP, evals, and inference, with guidance on what to prioritize and in what order. Read it below.