Generative
AI Engineer
Roadmap

Python is the lingua franca of AI. Master it deeply before touching any model or framework. Cover data types, OOP, list comprehensions, generators, decorators, virtual environments, and async/await. Then learn the scientific stack: NumPy for tensors, Pandas for data wrangling, and Matplotlib for visualization. Build 5 CLI tools from scratch before moving on — no shortcuts here.

You don't need a PhD — but you must understand the math behind what models do. Cover vectors, matrices, matrix multiplication, eigenvalues, and dot products (linear algebra). Understand derivatives, gradients, and the chain rule (calculus for backprop). Learn probability: Bayes' theorem, distributions, entropy, and KL divergence. Use 3Blue1Brown — nothing explains this better.

Understand supervised vs unsupervised learning, train/val/test splits, overfitting/underfitting, bias-variance tradeoff. Master: linear regression, logistic regression, decision trees, random forests, gradient boosting (XGBoost), and k-means clustering. Learn scikit-learn deeply. Build real projects: house price predictor, spam classifier, customer churn model. These fundamentals make you a better GenAI engineer — they give you intuition models don't.

Start by building a neural network from scratch in NumPy — forward pass, loss function, backpropagation, gradient descent. Then move to PyTorch: tensors, autograd, nn.Module, DataLoader, optimizers, training loops. Build CNNs for vision and RNNs/LSTMs for sequences. Understand batch normalization, dropout, learning rate schedules, and GPU training. Complete Andrej Karpathy's Neural Networks: Zero to Hero — it is the best deep learning course ever made for engineers.

The transformer architecture powers every major LLM. Understand self-attention, multi-head attention, positional encoding, encoder-decoder vs decoder-only architectures, tokenization (BPE, WordPiece), and the scaling laws that govern LLM behavior. Read the original "Attention Is All You Need" paper. Implement a mini GPT from scratch following Karpathy's makemore series. Understand BERT vs GPT paradigms, context windows, KV cache, and how inference works at scale.

Learn to integrate LLMs via APIs: OpenAI SDK, Anthropic SDK, Google Gemini SDK, and the unified LiteLLM layer. Master prompt engineering: zero-shot, few-shot, chain-of-thought (CoT), ReAct, structured outputs, and prompt chaining. Understand token limits, pricing, rate limiting, and streaming responses. Use LangChain and LlamaIndex for orchestration. Build a full Q&A bot, a summarizer, and a classification pipeline before moving on.

RAG is the backbone of 80% of enterprise AI products. Learn vector embeddings: what they are, how to generate them (OpenAI embeddings, sentence-transformers), and cosine similarity. Master vector databases: Pinecone, Weaviate, Chroma, pgvector. Build a full RAG pipeline: chunk documents, embed, store, retrieve, re-rank, and generate. Understand advanced RAG: hybrid search (BM25 + semantic), query rewriting, HyDE, contextual compression, and evaluation with RAGAS. Build a document Q&A system over a PDF corpus as a portfolio project.

When prompting isn't enough, you fine-tune. Learn supervised fine-tuning (SFT) with Hugging Face Transformers and TRL. Understand LoRA / QLoRA — parameter-efficient fine-tuning that runs on a single GPU. Use Unsloth for 2× faster training. Master instruction tuning datasets, RLHF basics, DPO (Direct Preference Optimization), and model merging. Work with Llama 3, Mistral, and Gemma. Deploy fine-tuned models with Ollama and vLLM for fast inference.

Agents are the next frontier. Understand the ReAct loop (Reason + Act), tool calling / function calling, and memory (in-context, episodic, semantic). Build agents with LangGraph for complex stateful workflows and CrewAI / AutoGen for multi-agent teams. Learn agent memory patterns, tool use (web search, code execution, APIs), self-reflection loops, and guardrails. Build a research agent, a code generation agent, and a multi-agent pipeline for a real problem. Evaluate agent performance — this is the hardest part.

Learn MLOps: experiment tracking with MLflow / Weights & Biases, model versioning, model registries, and CI/CD for ML pipelines. Deploy models as REST APIs using FastAPI + Docker. Learn inference optimization: quantization (GGUF, GPTQ), model distillation, and batching strategies. Use Hugging Face Spaces and Streamlit for demos. Build 2–3 complete AI products with live demos: a RAG chatbot, an AI agent, and a fine-tuned model endpoint. Document everything. Your GitHub and your demo links close offers — not your resume alone.