Build intelligent systems with LLMs and AI agents
AI Engineering is an emerging role focused on building practical AI applications using large language models (LLMs), AI agents, and modern AI tools. As an AI engineer, you will integrate LLMs into applications, design prompts, build AI agents, implement RAG (Retrieval Augmented Generation), fine-tune models, and create AI-powered features. This comprehensive roadmap covers LLM fundamentals, prompt engineering, LangChain, vector databases, AI agents, fine-tuning, AI APIs (OpenAI, Anthropic, Google), and AI application architecture. AI engineers are in extremely high demand as companies race to integrate AI into their products. The role requires strong programming skills, understanding of AI/ML concepts, creativity in prompt design, and ability to build production AI systems. Unlike ML engineers who focus on training models, AI engineers focus on using pre-trained models to build applications.
6-8 weeks
Supervised, unsupervised, deep learning overview
Architecture, training, transformers
GPT, BERT, how LLMs work, tokenization
Libraries, APIs, async programming
6-8 weeks
Zero-shot, few-shot, chain-of-thought
ReAct, tree of thoughts, self-consistency
Testing, iteration, evaluation
OpenAI, Anthropic, Google Gemini, API usage
8-10 weeks
Chains, prompts, output parsers
Conversation memory, buffer, summary
Function calling, tool use, agents
Debugging, monitoring, evaluation
8-10 weeks
Pinecone, Weaviate, Chroma, embeddings
Retrieval, generation, chunking strategies
PDF, web scraping, text extraction
Embeddings, similarity search, reranking
8-10 weeks
ReAct, planning, tool use
Agent collaboration, orchestration
Autonomous agents, task decomposition
CrewAI, AutoGen, custom agents
6-8 weeks
LoRA, QLoRA, instruction tuning
Quantization, distillation, inference
FastAPI, Docker, cloud deployment
Cost, latency, quality, observability