The Cloud Playbook: Complete AI Engineering Roadmap

Who Is This Guide For?

Perfect For:

• Platform Engineers with Kubernetes and cloud experience, ready to build AI-powered systems
• DevOps Engineers (2+ years) wanting to transition into the high-growth AI engineering field.
• Cloud Engineers familiar with AWS/GCP/Azure, looking to specialize in AI infrastructure and applications
• Site Reliability Engineers (SREs) who understand distributed systems and want to apply their  skills to AI systems
• DevSecOps Engineers interested in securing and operating AI applications at scale

Prerequisites:

• Infrastructure Experience: Comfortable with containers, orchestration, and cloud services
• Basic Programming: Some experience with Python, Go, or similar languages
• Systems Thinking: Understanding of distributed systems, APIs, and databases
• DevOps Mindset: Experience with automation, monitoring, and infrastructure-as-code

Learning Outcomes

You Will:

• Build Production AI Applications: Create intelligent systems using LLMs, embeddings, and vector databases
• Architect AI-First Systems: Design scalable applications with prompt engineering, RAG, and agentic workflows
• Implement MLOps Pipelines: Deploy, monitor, and maintain AI models in production environments
• Optimize AI Performance: Achieve efficient inference, cost optimization, and reliability for AI workloads
• Lead AI Initiatives: Make technical decisions on AI architecture and tooling for your organization

Career Impact:

• Salary Increase: AI Engineers earn 30-50% more than traditional backend/platform engineers
• Market Demand: AI Engineering roles growing 400% year-over-year with limited experienced candidates
• Future-Proof Career: Position yourself at the intersection of infrastructure and AI innovation
• Technical Leadership: Become the bridge between AI researchers and production systems

This roadmap transforms your existing infrastructure and systems expertise into AI engineering capabilities.