Python Programming Foundations
4-5 weeksSkills You'll Build
Python programming fundamentalsWorking with APIs and networking librariesAsync programming for distributed systemsData structures and algorithmsPackage management and environments
Network Engineer → AI Engineer
Network Engineer to AI Engineer: From Infrastructure to AI Systems
Your network engineering skills in distributed systems, protocols, and infrastructure are valuable in AI. This path transforms your understanding of system reliability, latency optimization, and scaling into AI engineering expertise. Build the AI systems that power modern applications.
6-8 months
Prerequisites
- Experience in network engineering or infrastructure
- Understanding of distributed systems and protocols
- Familiarity with Linux and system administration
- Experience with cloud platforms (AWS, Azure, GCP)
- Basic scripting experience (Bash, Python)
Your Learning Path
ML Fundamentals and AI Concepts
3-4 weeksSkills You'll Build
Machine learning core conceptsHow AI models work (high-level)AI system architecture overviewUnderstanding inference and latencyModel serving and scaling
LLM APIs and Integration
3-4 weeksSkills You'll Build
OpenAI and Claude API usageAPI design patterns for AIRate limiting and error handlingStreaming and real-time responsesCost and performance optimization
RAG Systems and Vector Databases
4-5 weeksSkills You'll Build
Vector databases and embeddingsDocument retrieval systemsBuilding knowledge-powered AIScaling retrieval infrastructureHigh availability for AI systems
AI System Deployment and Operations
4-5 weeksSkills You'll Build
Deploying AI applications (Docker, Kubernetes)Load balancing for AI servicesMonitoring and observabilityNetwork optimization for AISecurity for AI endpoints
Portfolio and Career Transition
2-3 weeksSkills You'll Build
Building AI infrastructure portfolioShowcasing systems expertiseAI engineer interviewsNetworking in AI communityPositioning infrastructure experience