AI Engineer Roadmap 2025
Master the skills needed to build production-ready AI applications. This roadmap covers machine learning fundamentals, large language models, RAG systems, vector databases, and deploying AI at scale on cloud platforms.
Your Progress
0%Prerequisites
- Python programming proficiency
- Basic understanding of statistics and linear algebra
- Familiarity with APIs and web development
- Basic cloud computing knowledge
What You'll Learn
- Build and deploy LLM-powered applications
- Implement RAG systems with vector databases
- Fine-tune and optimize AI models
- Deploy AI systems on AWS/cloud platforms
- Implement responsible AI practices
Python for AI/ML
Master Python libraries essential for AI development: NumPy, Pandas, and data manipulation.
Skills You'll Learn
Hands-on Projects
Data Analysis Pipeline
Build a data processing pipeline using Pandas and NumPy
Machine Learning Fundamentals
Understand core ML concepts: supervised/unsupervised learning, model evaluation, and common algorithms.
Skills You'll Learn
Deep Learning Basics
Learn neural networks, backpropagation, and deep learning frameworks.
Skills You'll Learn
Learning Resources
Hands-on Projects
Image Classifier
Build a CNN-based image classifier using PyTorch
LLM Fundamentals
Understand transformer architecture, attention mechanisms, and how LLMs work.
Skills You'll Learn
Prompt Engineering
Master the art of crafting effective prompts for LLMs.
Skills You'll Learn
Learning Resources
Hands-on Projects
Prompt Library
Create a library of optimized prompts for different use cases
Working with LLM APIs
Integrate OpenAI, Anthropic, and other LLM providers into applications.
Skills You'll Learn
Learning Resources
Hands-on Projects
AI Chatbot
Build a conversational AI chatbot with streaming responses
Text Embeddings
Understand and work with text embeddings for semantic search.
Skills You'll Learn
Learning Resources
Vector Databases
Learn to use vector databases like Pinecone, Weaviate, and pgvector.
Skills You'll Learn
Learning Resources
Hands-on Projects
Semantic Search Engine
Build a semantic search engine using embeddings and vector DB
Building RAG Systems
Implement complete RAG pipelines with document processing and retrieval.
Skills You'll Learn
Hands-on Projects
Document Q&A System
Build a RAG-based Q&A system for PDF documents
Model Serving & APIs
Deploy AI models as scalable APIs using FastAPI and cloud services.
Skills You'll Learn
Learning Resources
Hands-on Projects
AI API Service
Deploy an AI model as a production-ready API
MLOps Basics
Learn MLOps practices for managing AI systems in production.
Skills You'll Learn
AI System Monitoring
Monitor AI systems for performance, drift, and reliability.
Skills You'll Learn
Learning Resources
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