AI-Augmented Cloud Engineer
Cloud Infrastructure, Automation & Operations with AI
Provider
CloudSpace Academy
Duration
20-24 Weeks (Flexible Cohort Model)
Format
Instructor-Led, Live Online + Labs
Level
Intermediate (Career Switchers & Professionals)
Capstone
End-to-end AWS cloud project with AI-assisted operations
Prerequisites
Basic IT or networking knowledge recommended
Course Overview
The AI-Augmented Cloud Engineer (AWS) program prepares learners to design, deploy, secure, and operate cloud infrastructure using AI-assisted workflows.
Rather than focusing only on AWS services, this program teaches how modern cloud engineers work alongside AI tools to automate infrastructure, optimize costs, detect issues, and make better architectural decisions.
Students graduate with hands-on AWS experience, AI-enabled workflows, and portfolio-ready cloud projects aligned with real-world engineering roles. This is not an entry-level IT course.
Who This Program Is For
- IT professionals transitioning to cloud roles
- Systems and network engineers moving to AWS
- DevOps or support engineers upgrading skills
- Career switchers with technical foundations
- Veterans and transitioning service members
- This is not an entry-level IT course
Program Outcomes
- Design AWS architectures using AI-assisted analysis
- Deploy and manage infrastructure using Infrastructure as Code
- Use AI tools to optimize cost, performance, and availability
- Troubleshoot cloud issues with AI-supported workflows
- Secure AWS environments using modern best practices
- Monitor, scale, and operate production-style systems
- Communicate architectural decisions clearly and professionally
Detailed Syllabus
Phase 1
Cloud & AI Foundations (Weeks 1-3)
Topics Covered
- Modern cloud engineering roles (pre-AI vs AI-augmented)
- AWS global infrastructure fundamentals
- Core AWS services: EC2, S3, VPC, IAM
- Introduction to AI copilots for cloud engineers
- Prompting fundamentals for technical workflows
Outcome
Students understand AWS fundamentals and how AI fits into cloud engineering workflows.
Phase 2
AWS Networking, Identity & Security (Weeks 4-6)
Topics Covered
- VPC design and subnet architecture
- Routing, gateways, and connectivity
- IAM users, roles, policies, and least privilege
- Security groups and network ACLs
- AI-assisted security reviews and misconfiguration detection
Outcome
Students can design secure AWS environments and use AI to validate decisions.
Phase 3
Compute, Storage & Databases (Weeks 7-9)
Topics Covered
- EC2, Auto Scaling, and Load Balancing
- S3, EBS, EFS storage use cases
- RDS and managed database concepts
- High availability and fault tolerance
- AI-assisted architecture optimization
Outcome
Students can deploy resilient AWS workloads and optimize designs using AI guidance.
Phase 4
Infrastructure as Code & Automation (Weeks 10-13)
Topics Covered
- Infrastructure as Code principles
- AWS CloudFormation and/or Terraform
- Environment automation and repeatability
- AI-assisted IaC generation and validation
- Change management and version control
Outcome
Students can automate AWS infrastructure creation using IaC with AI support.
Phase 5
Monitoring, Cost Optimization & Operations (Weeks 14-17)
Topics Covered
- CloudWatch, logging, and metrics
- Monitoring availability and performance
- AWS cost models and billing fundamentals
- AI-assisted cost optimization strategies
- Incident response and root cause analysis
Outcome
Students can operate AWS environments efficiently and use AI to reduce costs and downtime.
Phase 6
Scaling, Reliability & Modern Architectures (Weeks 18-20)
Topics Covered
- Auto scaling and elasticity
- Serverless fundamentals (Lambda overview)
- Event-driven architectures
- Reliability engineering concepts
- AI-assisted capacity planning
Outcome
Students can design scalable and reliable cloud architectures.
Phase 7
Capstone Project (Weeks 21-24)
Capstone Requirements
Design and deploy a multi-tier AWS application, secure the environment using IAM and networking best practices, automate infrastructure using IaC, implement monitoring and cost controls, and use AI tools to assist design, troubleshooting, and optimization.
Final Deliverables
- Architecture diagrams
- Deployed AWS environment
- Documentation and decision rationale
- Portfolio-ready project
AI-Augmented Workflows Taught
Throughout the program, students learn how to:
- Use AI for architecture review
- Generate and validate IaC templates
- Troubleshoot logs and metrics
- Optimize AWS costs
- Improve documentation and communication
- Use AI as a tool, not a shortcut
Final Graduation Outcomes
Graduates leave with:
- Practical AWS cloud engineering experience
- AI-enabled cloud workflows
- Real-world project portfolio
- Confidence to operate modern AWS environments
- Readiness for cloud engineering and platform roles