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DevOps Cloud AI - Job Placement Mentorship Program
Digital Product

DevOps • Cloud • AI — Job Placement Mentorship Program

A practical, outcome-driven program to help you land a strong Cloud / DevOps / SRE / Platform / AI role. You get a 1:1 roadmap, solid DevOps + AWS / Azure foundations, 15 production-grade live projects, focused interview prep, and 10+ verified referrals — backed by live weekend sessions and midweek doubt-clearing sessions.

Fresh Batch Starting 16th May

Who This Program Is For

  • Beginners entering DevOps, Cloud, or SRE — including freshers and non-IT backgrounds
  • Testing, Support, Infra, and App Developers moving into DevOps / SRE / Cloud / Platform Engineering
  • Working DevOps and Cloud Engineers who want to fill gaps and move into senior or product-company roles
  • SREs wanting stronger observability, incident response, and AIOps skills
  • Platform Engineers building Kubernetes, GitOps, and internal developer platforms
  • System / Linux / Network Admins modernising into Cloud and Automation
  • Cloud and Data Engineers adding AI, MLOps, and GenAI
  • Mid-career engineers targeting senior, lead, or architect positions
  • Career switchers from Java, Python, .NET, or Database backgrounds
  • Job seekers and laid-off professionals needing projects, mentorship, mock interviews, and referrals

What You Will Achieve

  • Strong fundamentals + advanced skills across DevOps, Cloud, SRE, Platform, and AI
  • 15 production-grade projects on your GitHub
  • Job-ready Resume, LinkedIn, and GitHub portfolio
  • 10+ verified referrals across startups, product companies, and MNCs
  • Mock interviews, system design practice, and continuous interview readiness
  • 1:1 mentorship with a personalized roadmap and weekly progress reviews

Live Sessions — Weekly Cadence

You are never learning alone. Every week you get live time with mentors.

Weekend Live Project Sessions (Sat + Sun)

  • Hands-on, instructor-led implementation of the live projects
  • Architecture walkthroughs and real production patterns
  • Live troubleshooting on real environments, with code reviews

Midweek Doubt-Clearing Session

  • Unblocks you on assignments, errors, and concepts
  • Resume, LinkedIn, GitHub, and interview-question discussions
  • Career guidance, role targeting, and offer negotiation tips

Phase 0 — Personalized 1:1 Roadmap Session

Every learner starts with a 1:1 session to understand your background and goals, map a personalized learning path (DevOps / SRE / Cloud / Platform / Data + AI), define weekly milestones, and prioritise high-impact wins.

Phase 1 — Self-Paced Foundations (Recorded Material)

You complete DevOps + AWS / Azure Cloud fundamentals through structured recorded content. The recorded material is fully supported by the weekly doubt-clearing sessions, so you are never stuck on your own.

Tools covered: Linux, Git, Docker, Kubernetes, Helm, Terraform, Ansible, Jenkins, GitHub Actions, Prometheus, Grafana, ELK, AWS (IAM, EC2, VPC, S3, RDS, Lambda, API Gateway), Azure (Intro), Python and Shell scripting.

Phase 2 — 15 Live Industry-Grade Projects

You will design, build, demo, and ship every project end-to-end with mentor support.

Project 1 — DevOps Workflow & Agile Delivery Setup

  • Why: Every product team runs on Agile and DevOps workflows. Without knowing how Jira, Git branching, releases, and ceremonies actually work, you will struggle from day one.
  • You will learn: How real teams plan sprints, branch and release code, and collaborate across roles.
  • Stack: Jira, Confluence, Git, GitHub, branching strategies, Slack.

Project 2 — 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 3 — Golden AMI Pipeline

  • Why: Enterprises don't launch random images in production. They use hardened, patched, standardized images so every server is secure and consistent.
  • You will learn: Automate the build, hardening, and distribution of secure base images.
  • Stack: Packer, Ansible, Jenkins, AWS EC2, Systems Manager, CIS hardening.

Project 4 — Infrastructure Automation with Terraform & Ansible

  • Why: Clicking in the AWS console doesn't scale. Real teams provision and configure everything as code so it's versioned, reviewed, and reproducible.
  • You will learn: Design reusable Terraform modules and use Ansible for configuration in a production-style setup.
  • Stack: Terraform modules, Ansible, AWS VPC / EC2 / RDS / ALB, Remote State (S3 + DynamoDB).

Project 5 — Serverless Application Architecture

  • Why: Many modern apps run serverless to cut cost and scale automatically. Engineers who design event-driven systems are in high demand.
  • You will learn: Build a scalable, event-driven app with APIs, storage, and async processing.
  • Stack: AWS Lambda, API Gateway, DynamoDB, S3, EventBridge, SAM / Serverless Framework.

Project 6 — Kubernetes + GitOps Deployments

  • Why: Kubernetes is the default for microservices, and GitOps is how serious teams deploy — through Git, not manual kubectl.
  • You will learn: Deploy and manage microservices on Kubernetes using a declarative GitOps workflow.
  • Stack: Kubernetes (EKS), Helm, ArgoCD, Kustomize, Ingress, HPA.

Project 7 — Monitoring & Observability Platform

  • Why: If you can't see what's happening in production, you can't fix it. Every serious team invests heavily in metrics, logs, and traces.
  • You will learn: Design a complete observability stack across services.
  • Stack: Prometheus, Grafana, Loki, Alertmanager, CloudWatch, OpenTelemetry.

Project 8 — End-to-End CI/CD Pipeline

  • Why: Manual deployments are slow and risky. CI/CD is the heart of DevOps and almost every interview tests it.
  • You will learn: Build multi-stage pipelines for containerized apps with automated build, test, and deploy.
  • Stack: Jenkins / GitHub Actions, Maven / Gradle, Docker, Kubernetes, SonarQube, Slack.

Project 9 — MLOps Pipeline on AWS

  • Why: Companies are deploying ML models everywhere, and most data scientists can't operationalize them. Engineers who understand MLOps are paid a premium.
  • You will learn: Train, version, deploy, and monitor ML models in production.
  • Stack: AWS SageMaker, S3, MLflow, Docker, GitHub Actions, Model Registry, CloudWatch.

Project 10 — GenAI Application with AWS Bedrock

  • Why: Every company is trying to ship GenAI features. Engineers who can actually build and deploy them safely are rare and highly valued.
  • You will learn: Build a production-ready GenAI app with foundation models, APIs, and guardrails.
  • Stack: AWS Bedrock (Claude / Llama), Lambda, API Gateway, S3, Streamlit / React.

Project 11 — Cloud Cost Optimization & FinOps

  • Why: Cloud bills get out of control fast. Engineers who can reduce spend without breaking systems become trusted across the org.
  • You will learn: Analyse and reduce AWS spend through tagging, rightsizing, and automation.
  • Stack: AWS Cost Explorer, Budgets, Compute Optimizer, Trusted Advisor, Lambda, Athena.

Project 12 — Automated Testing in CI/CD

  • Why: A pipeline that doesn't test properly is just a fast way to ship bugs. Real teams gate releases on automated quality checks.
  • You will learn: Embed unit, integration, and end-to-end testing into CI/CD with proper reporting.
  • Stack: JUnit / PyTest, Selenium, Postman / Newman, JaCoCo, Allure Reports, Jenkins.

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: Add 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, Docker, Kubernetes.

Project 14 — 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.
  • You will learn: Use LLMs and ML to summarise logs, triage incidents, and suggest remediation.
  • Stack: Python, OpenAI / Bedrock, LangChain, OpenSearch / ELK, Prometheus, Slack / PagerDuty.

Project 15 — AI-Driven Data Pipeline (Cloud Data Engineering)

  • Why: Data + AI is where the highest-paid roles are heading. Knowing how to feed clean data into LLMs (RAG) is a skill very few engineers have.
  • You will learn: Build an end-to-end data pipeline that ingests, transforms, and serves data to an LLM-powered analytics layer.
  • Stack: AWS S3, Glue, Athena, Lambda, Airflow / Step Functions, Bedrock / OpenAI, Vector DB (OpenSearch / pgvector), Python.

For every project you get: source code, architecture diagrams, session recordings, README, and a portfolio-ready GitHub repo.

Phase 3 — Interview Preparation

  • Technical drills: DevOps, Cloud, Kubernetes, CI/CD, Terraform, Linux, Scripting
  • Scenario and system design rounds: real production problem-solving
  • SRE and Platform rounds: SLO/SLI design, incident response, on-call readiness
  • Behavioural and HR rounds: STAR method, leadership principles
  • Mock interviews: recorded, reviewed, and debriefed
  • Curated question bank from real hiring loops
  • Resume, LinkedIn, and GitHub optimization

Phase 4 — Referrals & Placement Support

  • 10+ verified referrals across startups, product companies, and MNCs
  • Active job leads in the community channel
  • Placement coordinator support and follow-ups
  • Weekly progress tracking until you land the role
  • Continued support post-offer (negotiation and onboarding)

Program Highlights

  • Outcome-based, not time-based — stay until you achieve your goal
  • Join anytime — no rigid batches; repeat the program multiple times until you get your result
  • Live weekend sessions + midweek doubt-clearing
  • 1:1 mentorship from a senior DevOps Cloud AI Architect
  • Dedicated Slack community with mentors, peers, and hiring partners
  • Mentorship + Project Completion Certificates (GitHub verified)
  • Work Experience Letter to support your applications
  • Lifetime access to recorded content, project code, and templates

UPI Payments as backup- UPI to 9888055959 or direct2sanjeev@icici

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DevOps Cloud AI - Job Placement Mentorship Program with Sanjeev Kumar