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From zero to production-grade AI systems. 17 phases, 51 projects, every skill you need.
Source: github.com/PrinceSinghhub
Start from scratch. Build Python, math, and ML foundations before touching LLMs.
You know Python and some ML. Jump to ML/DL fundamentals, then straight into LLM engineering.
You understand ML. Go directly to transformers, multi-LLM orchestration, RAG, and agents.
AI Engineer vs ML Engineer. Market demand 2026. What you need to know.
Including async/await for AI APIs — most roadmaps miss this.
Linear algebra, calculus, probability, optimization.
Fundamentals for understanding LLMs. Supervised, unsupervised, evaluation.
Neural networks, CNNs, RNNs — foundation for understanding transformers.
Architecture deep dive. Attention mechanisms, BERT, GPT internals.
ALL major APIs: OpenAI, Claude, Gemini, Mistral, Groq, NVIDIA.
Routing, fallbacks, MCP, LangGraph, LangChain, CrewAI, AutoGen.
HyDE, reranking, hybrid search, advanced retrieval techniques.
Build autonomous agents that use tools, plan, and execute.
LoRA, QLoRA, DPO, RLHF — customize models for your domain.
Diffusion models, multimodal, voice synthesis, video generation.
Production deployment, monitoring, Kubernetes, CI/CD for AI.
Interview-ready architecture patterns for real AI systems.
Postgres for AI: vector search, embeddings, hybrid queries.
vLLM, GGUF, SLMs — run models fast and cheap.
RLHF, DPO, PPO — train models from human feedback.
Governance, bias, safety testing, responsible deployment.
Each phase includes 3 projects at increasing difficulty: Easy, Medium, and Hard. Complete all 51 to build a production-grade AI portfolio.
Full roadmap breakdown on YouTube — watch alongside this guide.
Watch: Ultimate AI Engineer Roadmap 2026Pre-built skills, agents, memory systems, and hooks that map to each phase of this roadmap. Stop prompting. Start building systems.
Get AI Brain Pro — $97Roadmap structure adapted from github.com/PrinceSinghhub. All commentary and product recommendations are original.