AI Engineer Roadmap
Phase 1:Python & API Foundations
beginner4 weeksPython Fundamentals
Variables, functions, data structures, OOP, and file I/O. This is the Python you need before touching any AI library.
beginner1 weekHTTP & REST APIs
How APIs work, making requests with Python's requests library, handling JSON, and authenticating with API keys.
beginner1 weekEnvironment Setup
Virtual environments, .env files for secrets, pip, and the developer workflow for AI projects.
beginner1 weekGit & GitHub
Version control for your AI projects: committing, branching, and sharing code.
Phase 2:LLM Fundamentals
beginner1 weekHow LLMs Work
Tokens, embeddings, the transformer architecture, context windows, and why prompting matters.
beginner2 weeksPrompt Engineering
System prompts, few-shot examples, chain-of-thought, structured output, and getting reliable responses from models.
intermediate1 weekOpenAI API
Chat completions, function calling, streaming, and cost management with the OpenAI API.
intermediate2 weeksOpen Source Models
Run models locally with Ollama, use Hugging Face models, and understand when to use open vs closed models.
Phase 3:Building AI Applications
intermediate3 weeksLangChain
Chains, prompts, document loaders, text splitters, and the LangChain ecosystem for AI application development.
intermediate2 weeksVector Databases
Embeddings, similarity search, and vector stores, the core of RAG and semantic search systems.
intermediate3 weeksRAG Systems
Retrieval-Augmented Generation: connecting AI to your own documents for accurate, grounded answers.
advanced3 weeksAI Agents
Tool use, function calling, planning, and building agents that take multi-step actions autonomously.
Phase 4:Advanced Techniques
advanced2 weeksFine-tuning
When to fine-tune vs. prompt, preparing training data, running fine-tuning jobs with OpenAI or Hugging Face PEFT.
advanced2 weeksEvaluation & Testing
LLM evaluation frameworks, red-teaming, regression testing, and measuring model quality consistently.
intermediate1 weekStructured Outputs
JSON mode, function calling, Pydantic validation, and guaranteeing predictable AI outputs in production.
Phase 5:Production & Deployment
intermediate2 weeksAPI Design for AI
Build FastAPI or Flask backends that expose your AI functionality with streaming, caching, and rate limiting.
advanced1 weekCost & Latency Optimisation
Caching, prompt compression, model selection, and monitoring spend to keep AI apps economical.
advanced2 weeksObservability & Monitoring
Log LLM calls, track quality over time, and use tools like LangSmith or Langfuse to debug AI systems.
advanced6 weeksPortfolio AI Projects
Build 2-3 end-to-end AI applications: a RAG chatbot, a document summariser, or an autonomous agent.
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