AI Engineer Roadmap
Build intelligent systems with LLMs and AI agents
About This Roadmap
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.
Prerequisites
- Programming skills (Python)
- Basic ML understanding
- API integration experience
- Problem-solving mindset
What You'll Learn
Complete Learning Path
6-8 weeks
1ML Basics
Supervised, unsupervised, deep learning overview
2Neural Networks
Architecture, training, transformers
3LLM Basics
GPT, BERT, how LLMs work, tokenization
4Python for AI
Libraries, APIs, async programming
6-8 weeks
1Prompt Basics
Zero-shot, few-shot, chain-of-thought
2Advanced Prompting
ReAct, tree of thoughts, self-consistency
3Prompt Optimization
Testing, iteration, evaluation
4AI APIs
OpenAI, Anthropic, Google Gemini, API usage
8-10 weeks
1LangChain Basics
Chains, prompts, output parsers
2Memory
Conversation memory, buffer, summary
3Tools & Agents
Function calling, tool use, agents
4LangSmith
Debugging, monitoring, evaluation
8-10 weeks
1Vector Databases
Pinecone, Weaviate, Chroma, embeddings
2RAG Architecture
Retrieval, generation, chunking strategies
3Document Processing
PDF, web scraping, text extraction
4Semantic Search
Embeddings, similarity search, reranking
8-10 weeks
1Agent Architecture
ReAct, planning, tool use
2Multi-Agent Systems
Agent collaboration, orchestration
3AutoGPT/BabyAGI
Autonomous agents, task decomposition
4Agent Frameworks
CrewAI, AutoGen, custom agents
6-8 weeks
1Fine-tuning
LoRA, QLoRA, instruction tuning
2Model Optimization
Quantization, distillation, inference
3Deployment
FastAPI, Docker, cloud deployment
4Monitoring
Cost, latency, quality, observability
Tools & Technologies
Career Opportunities
- AI Engineer
- LLM Engineer
- AI Application Developer
- Prompt Engineer
- AI Product Engineer
- AI Architect