AI Engineer Roadmap
AI Engineering is the art of building intelligent products using existing AI models — the OpenAI API, open-source models on Hugging Face, LangChain agents, and RAG pipelines. This roadmap is distinct from the Machine Learning path: you're not training models from scratch, you're building products with the models that already exist. The demand for AI Engineers is enormous and growing faster than any other role in tech.
Phase 1: Phase 1 — Python & API Foundations
Python Fundamentals
Variables, functions, data structures, OOP, and file I/O — the Python you need before touching any AI library.
HTTP & REST APIs
How APIs work, making requests with Python's requests library, handling JSON, and authenticating with API keys.
Environment Setup
Virtual environments, .env files for secrets, pip, and the developer workflow for AI projects.
Git & GitHub
Version control for your AI projects — committing, branching, and sharing code.
Phase 2: Phase 2 — LLM Fundamentals
How LLMs Work
Tokens, embeddings, the transformer architecture, context windows, and why prompting matters.
Prompt Engineering
System prompts, few-shot examples, chain-of-thought, structured output, and getting reliable responses from models.
OpenAI API
Chat completions, function calling, streaming, and cost management with the OpenAI API.
Open Source Models
Run models locally with Ollama, use Hugging Face models, and understand when to use open vs closed models.
Phase 3: Phase 3 — Building AI Applications
LangChain
Chains, prompts, document loaders, text splitters, and the LangChain ecosystem for AI application development.
Vector Databases
Embeddings, similarity search, and vector stores — the core of RAG and semantic search systems.
RAG Systems
Retrieval-Augmented Generation: connecting AI to your own documents for accurate, grounded answers.
AI Agents
Tool use, function calling, planning, and building agents that take multi-step actions autonomously.
Phase 4: Phase 4 — Advanced Techniques
Fine-tuning
When to fine-tune vs. prompt, preparing training data, running fine-tuning jobs with OpenAI or Hugging Face PEFT.
Evaluation & Testing
LLM evaluation frameworks, red-teaming, regression testing, and measuring model quality consistently.
Structured Outputs
JSON mode, function calling, Pydantic validation, and guaranteeing predictable AI outputs in production.
Phase 5: Phase 5 — Production & Deployment
API Design for AI
Build FastAPI or Flask backends that expose your AI functionality with streaming, caching, and rate limiting.
Cost & Latency Optimisation
Caching, prompt compression, model selection, and monitoring spend to keep AI apps economical.
Observability & Monitoring
Log LLM calls, track quality over time, and use tools like LangSmith or Langfuse to debug AI systems.
Portfolio AI Projects
Build 2–3 end-to-end AI applications: a RAG chatbot, a document summariser, or an autonomous agent.