15 Live Hands-On DevOps, Cloud & AI Projects Bootcamp

Mentor: DevOps Cloud AI Architect

A focused live bootcamp built around 15 industry-grade DevOps, Cloud, and GenAI projects. Live on Zoom, fully interactive, with weekly doubt-clearing sessions and continuous WhatsApp support. Small batches (~25 learners) for personal attention.

Fresh Batch Starting 23th May 2026

Who This Is For

Professionals targeting roles like DevOps Engineer, Cloud Engineer, Platform Engineer, SRE, AI Engineer, or Cloud Architect.

These are not YouTube-style mini-projects — every session is built on real-world enterprise use cases, simplified for clear understanding.

Program Highlights

• 15 real industry projects based on actual corporate implementations
• Weekly doubt-clearing sessions + dedicated WhatsApp support
• Personal help when you’re stuck — debugging and setup support
• Small group (~25 learners) for focused mentorship
• Lifetime access to presentations, source code, and recordings
• Join anytime — sessions run on a cyclic schedule, every project is standalone
• Repeat the program multiple times at no extra cost until you get your result
• Dedicated sessions on roadmap, resume, and interview prep using these projects
• Concepts apply to AWS, Azure, and GCP — not tied to one provider

Prerequisites

Basic knowledge of DevOps and Cloud technologies.

Batch Format & Schedule

• Mode: Live on Zoom — fully interactive, screen sharing and Q&A
• Project sessions: Every Saturday, 5:00 PM – 8:00 PM IST (~3 hours) — live coding, architecture walkthroughs, hands-on implementation
• Doubt-clearing sessions: Every Friday, 5:00 PM – 6:30 PM IST (~1.5 hours) — live debugging, concept clarification, issue resolution
• Each project is taught as a live, independent session — join anytime and catch up cyclically.

The 15 Projects

Project 1 — DevOps Kickstart: Agile Workflow & Team Collaboration

• Why: Real teams run on Agile and DevOps workflows — sprint planning, Git branching, PR reviews. Without this, you struggle from day one on the job.
• You will learn: Sprint planning and Jira workflows, GitFlow branching, PR reviews and code ownership, the CI/CD mindset, and the day-to-day reality of DevOps / Cloud roles.
• Stack: GitHub, Jira, Agile, CI/CD concepts, Slack.

Project 2 — AI for DevOps: GitHub Copilot & AI-Assisted Automation

• Why: DevOps engineers are now expected to use AI tools to move faster. Knowing how to use Copilot for Terraform, Docker, and Kubernetes is becoming a baseline skill.
• You will learn: Copilot for Terraform / Docker / K8s, AI-assisted debugging, productivity automation, and how to set up the workstation of an AI-assisted DevOps engineer.
• Stack: GitHub Copilot, AI DevOps tools.

Project 3 — Dockerized Microservices: Production-Ready Containers

• Why: Almost every modern app runs in containers. Building secure, optimised, multi-service Docker setups is a core DevOps skill.
• You will learn: Multi-stage Docker builds, image size optimization, secure Dockerfiles, and a multi-service app with Docker Compose.
• Stack: Docker, Dockerfile (multi-stage), Docker Compose.

Project 4 — AWS Multi-Account Governance with Control Tower

• Why: Companies don’t run on a single AWS account — they have separate accounts for dev, QA, prod, security, and billing. You need to set this up and govern it safely.
• You will learn: Build a secure multi-account landing zone with guardrails, SSO, and centralized billing.
• Stack: AWS Organizations, Control Tower, IAM Identity Center, SCPs, CloudTrail, Config.

Project 5 — Enterprise CI/CD Pipelines with GitHub Actions

• Why: Manual deployments don’t scale. Companies expect engineers to design CI/CD pipelines that build, test, scan, and deploy automatically.
• You will learn: Build–test–scan–deploy pipelines, self-hosted runners (real enterprise pattern), secrets and environment protection.
• Stack: GitHub Actions, Docker, self-hosted runners, Kubernetes.

Project 6 — Terraform Foundations: Infrastructure as Code

• Why: Clicking in the cloud console doesn’t scale and isn’t repeatable. Real teams provision everything as code — versioned, reviewed, reproducible.
• You will learn: Modular Terraform design, remote state and state locking, and clean dev / stage / prod separation.
• Stack: Terraform, AWS, Remote State (S3 + DynamoDB).

Project 7 — Terraform in CI/CD: Resilient Infrastructure

• Why: Writing Terraform is easy. Running it safely in CI/CD with rollback and drift handling is what separates juniors from seniors.
• You will learn: Terraform inside CI/CD pipelines, rollback-safe deployments, and drift detection and recovery.
• Stack: Terraform, GitHub Actions, Docker.

Project 8 — Golden AMI Pipeline with Packer & Terraform

• Why: Enterprises don’t launch random images in production. They use hardened, patched, standardised “golden” images so every server is secure and consistent.
• You will learn: Golden AMI creation, security hardening, and canary deployment strategy.
• Stack: Packer, Terraform, EC2, Ansible, CIS hardening.

Project 9 — Managing 100+ Servers with Ansible

• Why: Real environments don’t have 2 servers — they have hundreds. You need automation that handles scale and compliance, not SSH-and-pray.
• You will learn: Ansible roles and playbooks, dynamic inventory on AWS, and security and compliance automation.
• Stack: Ansible, dynamic inventory, SSH hardening.

Project 10 — Kubernetes Production Cluster Deployment

• Why: Kubernetes is the default platform for microservices. Deploying real apps on it is what companies actually pay for — not just `kubectl run`.
• You will learn: Helm-based deployments, Ingress and services, autoscaling, and a secure scalable K8s architecture.
• Stack: Kubernetes, EKS, Helm, Ingress, HPA.

Project 11 — GitOps with Argo CD

• Why: Modern teams don’t deploy with manual kubectl — they deploy through Git. GitOps with Argo CD is now an interview-favourite topic.
• You will learn: Declarative deployments, sync strategies, and blue-green / canary releases.
• Stack: Argo CD, GitHub, Kubernetes, Helm, Kustomize.

Project 12 — Monitoring & Observability Stack

• Why: You can’t fix what you can’t see. Every serious team invests heavily in metrics, dashboards, and alerts.
• You will learn: Metrics, dashboards, and alerts; SLOs / SLAs; and deployment-aware monitoring.
• Stack: Prometheus, Grafana, Loki, Alertmanager, CloudWatch, OpenTelemetry.

Project 13 — Secure DevOps CI/CD (DevSecOps)

• Why: Security can’t be bolted on at the end. Companies expect security checks, secret management, and image scanning baked into the pipeline.
• You will learn: Code quality gates, dependency and image scanning, secrets management, and a Dev → QA → Prod artifact promotion flow.
• Stack: Jenkins / GitHub Actions, SonarQube, Snyk, Trivy, HashiCorp Vault, GitHub Secrets, JFrog / Nexus / ECR, OWASP ZAP.

Project 14 — GenAI Application with AWS Bedrock + LLM Agent

• Why: Every company is shipping GenAI features. Engineers who can build, deploy, and operate LLM-based apps are in very high demand.
• You will learn: Build an LLM-based API, set up CI/CD for AI applications, and apply MLOps practices to a production GenAI app.
• Stack: Amazon Bedrock, LangChain, API Gateway, Lambda, S3, Streamlit / React.

Project 15 — AI-Powered Log & Incident Analysis (AIOps)

• Why: Production generates more logs and alerts than humans can read. LLMs are now used to triage incidents and suggest fixes — a key edge for modern SRE / DevOps engineers.
• You will learn: Use LLMs and ML to summarise logs, triage incidents, and suggest remediation.
• Stack: Python, OpenAI / AWS Bedrock, LangChain, OpenSearch / ELK, Prometheus, Slack / PagerDuty.

For every project you get: presentations, source code, architecture diagrams, session recordings, and lifetime access.

What You Walk Away With

• 15 industry-grade projects you can showcase on your resume and GitHub
• Confidence with the full modern DevOps + Cloud + GenAI + AIOps stack
• Clear understanding of how real engineering teams work
• Roadmap, resume, and interview-prep guidance built into the program

DEVOPS CLOUD – JOB PLACEMENT / MENTORSHIP PROGRAM