Become a job-ready Multi-Cloud Engineer with AI
Master AWS, Azure and GCP with Docker, Kubernetes and MLOps — then go further and deploy LLMs and generative AI on the cloud, and run agentic AIOps that keep your systems healthy.
Still deciding? Talk to a mentor first.
A free 15-minute call to map your path to a job-ready Multi-Cloud Engineer with AI — honest guidance, zero pressure.
- Personalised learning roadmap
- Full syllabus, batch dates & fees
- Scholarships & no-cost EMI options
- Placement & interview support
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Cloud runs everything, now
it also runs the AI
Almost every modern application lives on AWS, Azure or Google Cloud — and most serious organisations now use more than one. On top of that, teams everywhere are racing to host LLMs, ship generative-AI features and automate operations with AI agents. Learn to build, deploy and operate across all three clouds, and to run AI workloads and agentic AIOps on them, and you become the engineer platforms teams are scrambling to find.
Hands-on across AWS, Azure and GCP — the three clouds most enterprises actually run on.
Host LLMs, wire up RAG infrastructure and run agentic AIOps — skills teams are hiring for right now.
Portfolio builds — including a deployed Gen-AI app — you can walk an interviewer through line by line.
Compute, networking, containers, IaC, CI/CD, MLOps and AI ops — the complete cloud skill set.
Built for four kinds
of people
Whatever you’re starting from, the course begins at fundamentals and takes you to job-ready — with the cloud and AI skills employers now expect.
Students & freshers
Final-year students and recent graduates who want a real cloud skill and a portfolio before applying.
Non-IT career switchers
Coming from a non-technical background. We start at fundamentals — Linux and networking from the ground up.
Working professionals
Sysadmins, support and testing engineers moving into cloud, DevOps or SRE roles. Weekend batches available.
Developers going cloud-native
You write code but need to own deployment, containers, pipelines and how AI workloads run in production.
Four phases, twelve modules,
one AI-ready cloud engineer
Every module ends in hands-on work. Nothing here is theory-only.
Cloud foundations
Start from the operating system and network up, then get hands-on with the two biggest clouds — AWS and Azure — from first principles.
Linux & networking fundamentals
Weeks 1–3Linux command line·Users & permissions·Shell scripting basics·Processes & services·TCP/IP & DNS·Subnets & routing·SSH & firewalls·HTTP & load balancing
AWS core services
Weeks 3–5IAM & identity·EC2 & EBS·VPC & security groups·S3 storage·RDS & databases·Elastic Load Balancing & auto-scaling·Lambda & serverless·CloudWatch basics
Azure core services
Weeks 5–7Azure Resource Manager·Entra ID & RBAC·Virtual Machines·Virtual Networks·Blob Storage·Azure SQL·App Service & Functions·Azure Monitor
Multi-cloud & containers
Add the third cloud, then learn to package and orchestrate workloads that run the same way anywhere — the heart of a multi-cloud strategy.
GCP core services
Weeks 7–9Projects & IAM·Compute Engine·VPC networking·Cloud Storage·Cloud SQL & BigQuery basics·Cloud Run & Functions·Operations suite·Comparing AWS, Azure & GCP
Docker & containerisation
Weeks 9–11Images & containers·Dockerfiles·Volumes & networks·Docker Compose·Multi-stage builds·Registries (ECR, ACR, Artifact Registry)·Image security·Best practices
Kubernetes orchestration
Weeks 11–13Pods, deployments & services·ConfigMaps & secrets·Ingress·Scaling & rollouts·Helm·Managed clusters (EKS, AKS, GKE)·Persistent storage·Health & self-healing
Automation & delivery
Stop clicking in consoles. Define infrastructure as code, ship it through pipelines and watch it in production with real observability.
Infrastructure as Code with Terraform
Weeks 13–15HCL syntax·Providers & resources·Variables & outputs·State & remote backends·Modules·Provisioning across AWS, Azure & GCP·Workspaces·Terraform best practices
CI/CD pipelines
Weeks 15–17Git & GitHub workflows·Jenkins pipelines·GitHub Actions·Build, test & deploy stages·Container image pipelines·Deploying to Kubernetes·Secrets management·GitOps intro
Monitoring & observability
Weeks 17–19Metrics, logs & traces·Prometheus·Grafana dashboards·Alerting & on-call·Centralised logging·SLOs & error budgets·Cloud-native monitoring tools·Incident response
AI on the cloud & career
Put AI to work on your infrastructure — deploy models, host LLMs and run agentic AIOps — then ship a capstone and get interview-ready.
MLOps & deploying models
Weeks 19–21ML lifecycle on the cloud·Model packaging & serving·Containerising models·GPU compute basics·Model registries·CI/CD for models·Monitoring & drift·Scaling inference
Gen AI & Agentic AI on the cloud
Weeks 21–24Hosting & serving LLMs·RAG infrastructure & vector databases·Embeddings pipelines·AI agents for operations (AIOps)·Model Context Protocol (MCP)·Cost, scaling & guardrails·Secure AI deployment·Responsible AI
Multi-cloud capstone & interview prep
Weeks 24–26End-to-end capstone·Multi-cloud architecture·AI-driven cloud automation·Cost optimisation·Cloud security review·Portfolio & resume·Scenario & whiteboard rounds·Mock interviews
Want the full module-by-module syllabus?
We’ll send the complete curriculum PDF along with batch dates and fee details.
You graduate with a
portfolio, not just notes
Four complete builds — including a deployed Gen-AI app — created the way real teams work: as code, version controlled, reviewed and shipped to the cloud.
Multi-cloud CI/CD deployment
Provision infrastructure with Terraform and ship an application through a Jenkins / GitHub Actions pipeline to more than one cloud, fully automated.
Kubernetes microservices platform
Containerise a set of microservices and deploy them to a managed Kubernetes cluster with ingress, auto-scaling, secrets and self-healing.
Gen-AI app with RAG infrastructure
Deploy an LLM-powered application on the cloud — hosted model, vector database and retrieval-augmented generation, served behind a scalable API.
Capstone — AI-driven cloud automation
Build an agentic AIOps workflow that monitors your infrastructure and acts — detecting incidents, remediating and reporting — with mentor review throughout.
The complete
cloud stack
Cloud platforms
- AWS
- Azure
- Google Cloud
- IAM & identity
- Compute & storage
- Managed databases
Containers & orchestration
- Docker
- Docker Compose
- Kubernetes
- Helm
- EKS / AKS / GKE
- Container registries
Infrastructure as Code
- Terraform
- HCL
- Modules & state
- Remote backends
- Cloud provisioning
- GitOps
CI/CD & automation
- Git & GitHub
- Jenkins
- GitHub Actions
- Pipelines
- Secrets management
- Shell scripting
AI & Generative AI
- LLM hosting
- RAG infrastructure
- AI agents / AIOps
- MLOps
- Vector databases
- Model Context Protocol (MCP)
Monitoring & tools
- Prometheus
- Grafana
- Cloud-native monitoring
- Logging & tracing
- Linux
- Networking
Roles you can apply for
when you finish
The same skill set opens several doors — from core cloud roles to the fast-growing MLOps and AI-operations titles our learners now target.
Cloud Engineer
Design, provision and operate workloads across AWS, Azure and GCP for real teams.
DevOps Engineer
Own pipelines, infrastructure as code and container platforms end to end.
Multi-Cloud Architect
Design portable, resilient architectures that span more than one cloud provider.
MLOps Engineer
Package, deploy and monitor machine-learning and LLM workloads in production.
Site Reliability Engineer (SRE)
Keep systems reliable with observability, SLOs, automation and incident response.
Cloud AI Engineer
Host LLMs, build RAG infrastructure and run agentic AIOps on the cloud.
Resume & portfolio
We rewrite your resume around your builds and set up a GitHub that reads well to a reviewer.
Interview drills
Cloud, Kubernetes, Terraform, CI/CD and AI-ops question banks, practised until the answers are automatic.
Mock interviews
Technical and HR rounds with working engineers, followed by honest feedback.
Hiring introductions
Your profile shared with our hiring-partner network as suitable roles open up.
Pick a batch
that fits your week
EDIT: replace the dates and timings below with your actual batch schedule.
Weekday morning batch
Weekday evening batch
Weekend batch — for working professionals
What every enrolment includes
- Live instructor-led sessions with recordings for revision
- Small batches with dedicated doubt-clearing time
- Four portfolio builds — including a deployed Gen-AI app — with mentor code review
- Resume building and mock interview rounds
- Placement assistance that continues after your last class
- Course completion certificate
Everything you’re
probably wondering
EDIT the answers to match DSU’s actual policies before publishing.
No. Module 1 starts with Linux and networking fundamentals from first principles, and every cloud is taught from the ground up. A large share of our learners come from non-cloud backgrounds. Basic comfort with computers is enough to begin.
The program runs 6–7 months — the time covers all three clouds plus the MLOps and Gen-AI / agentic-AI modules. Weekday batches are two hours a day; weekend batches are three hours per day across Saturday and Sunday. Plan for a similar amount of hands-on lab time outside class — that’s where the learning consolidates.
EDIT: state your fee here, along with any instalment or EMI options. Being upfront about pricing on this page will improve enquiry quality — people who can’t find a price often assume the worst and leave.
We provide placement assistance — resume preparation, mock interviews and introductions to our hiring-partner network — and that support continues after your final class. We don’t promise a guaranteed job, because no honest training provider can. What we can promise is that you’ll finish interview-ready with real cloud builds to show. EDIT this to match your policy exactly.
We work within the free tiers of AWS, Azure and GCP wherever possible and teach you to shut resources down and control spend — a real skill in itself. EDIT: describe any lab credits, sandbox accounts or cost guidance DSU provides.
Every class is recorded and available to you, so you can catch up on your own schedule. You can also raise anything unclear in the dedicated doubt-clearing sessions.
No. The AI modules focus on operating AI on the cloud, not researching it — you learn to deploy and serve models, host LLMs, build RAG infrastructure and run agentic AIOps. There’s no heavy maths. The emphasis is on the infrastructure, automation and reliability that make AI workloads run in production — exactly the skills that set a cloud engineer apart today.
Become an AI-ready cloud engineer
Book a free counselling session. We’ll walk you through the syllabus, batch options and fees — and tell you honestly whether this course is right for you.