DevOps Roadmap 2026: How to Become a DevOps Engineer

• TL;DR: A DevOps roadmap takes you through 7 stages:
  (1) Linux & networking
  |(2) Bash & Python scripting
  (3) Git
  (4) Docker,
  (5) CI/CD
  (6) cloud & Terraform
  (7) Kubernetes & monitoring, followed by projects and interview prep.
  A complete beginner needs about 9–12 months at 10–12 hours a week. A developer or sysadmin needs about 4–6 months. This guide gives you the month-by-month plan, the tools for each stage, a project per stage, India salaries and interview prep.

Companies are moving toward cloud-native systems, faster release cycles, AI-assisted operations, and platform engineering and they need engineers who can automate infrastructure, reduce deployment failures, and help teams ship faster without compromising security.

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Who This DevOps Roadmap Is For

This roadmap is designed for learners who want a practical, job-focused path into DevOps, cloud infrastructure, SRE, or platform engineering.

1. Complete Beginners

If you are new to tech or switching careers, start with Linux, networking, Git, and Python/Bash scripting before jumping into Docker or Kubernetes. A beginner can become job-ready in in 9–12 months with consistent hands-on practice.

2. System Administrators

If you already manage servers manually, your next step is automation. Focus on scripting, Git, Docker, CI/CD, Terraform, and Kubernetes. Sysadmins can often transition faster because they already understand operating systems, networking, and production environments.

3. Backend Developers

If you already write backend code, focus on infrastructure, Linux administration, cloud networking, containers, deployment pipelines, and observability. Developers can often become DevOps-ready in 4–6 months with targeted upskilling.

4. CS/IT Students

If you are a student, DevOps gives you a strong career path beyond traditional frontend/backend development. Build portfolio projects using GitHub Actions, Docker, Terraform, AWS, and Kubernetes to stand out in internships and entry-level roles.

Watch: The DevOps career roadmap
How to move into DevOps step by step: the skills, tools and projects to focus on at each stage.

Prerequisite Checklist

Before starting this roadmap, you should have:

• Basic command-line familiarity
• Logical problem-solving ability
• Willingness to write scripts and debug errors
• Basic understanding of how web applications work
• Interest in automation, infrastructure, cloud, and reliability

You do not need to be an expert programmer before starting DevOps, but you must be willing to learn scripting.

DevOps Roadmap at a Glance

The 7 stages below assume 10–12 hours of study a week. Each stage has one milestone project finish it before moving on.

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What is DevOps?

DevOps is a cultural and technical approach that brings together software development (Dev) and IT operations (Ops). Its goal is to help teams build, test, release, deploy, monitor, and improve software faster and more reliably.

Instead of developers writing code and operations teams manually deploying it later, DevOps encourages both teams to collaborate throughout the software development lifecycle. Automation is central to DevOps: infrastructure is defined as code, builds and tests run automatically, containers package applications consistently, and monitoring systems provide continuous feedback.

The modern DevOps lifecycle works as a continuous loop:

Plan → Code → Build → Test → Release → Deploy → Monitor → Feedback

When done well, DevOps helps organizations achieve faster releases, fewer production failures, better scalability, stronger security, and faster recovery from incidents.

Stage 1 (Month 1–2): Linux, Networking and the Command Line

Linux comes first because every tool in the rest of this roadmap Docker, Kubernetes, CI/CD runners, cloud servers is a Linux machine underneath. Start by understanding the Linux operating system features that make it the default for servers.

Most production servers, containers, and Kubernetes nodes run on Linux. DevOps engineers must be comfortable working inside Linux systems without relying on a graphical interface.

Key Linux Concepts

Learn the Linux file system structure, permissions, users and groups, environment variables, package managers, system services, and process management. Understand commands such as ls, cd, chmod, chown, ps, top, kill, systemctl, journalctl, df, du, and free.

Also learn how Linux handles networking. You should know how to inspect open ports, test connectivity, view IP addresses, read routing tables, and troubleshoot DNS issues.

Shell Scripting

Bash scripting helps you automate tasks such as log rotation, disk cleanup, backups, service restarts, and system checks. Learn variables, loops, conditions, functions, exit codes, and scheduled jobs.

DevOps engineers spend a large part of their day inside terminals. You must be able to navigate, inspect, edit, search, transfer, and automate files entirely through the command line.

CLI Skills to Practise

Start with file navigation and manipulation: pwd, ls, cd, cp, mv, rm, mkdir, touch, cat, less, and tail. Then learn text processing with grep, awk, sed, sort, uniq, and cut. These tools help you analyze logs quickly during production debugging.

Next, practise process and network commands such as ps, top, htop, kill, netstat, ss, curl, ping, traceroute, and dig. These commands are essential when debugging why a service is slow, unreachable, or failing.

Finally, learn remote access and file transfer using ssh, scp, and rsync. These are used to access servers, copy logs, and automate remote tasks.

Networking Basics for DevOps

(moved here from old Step 9 — networking belongs with Linux fundamentals, before you touch cloud)

Cloud platforms are computer networking with a console on top, so revise the concepts now:

Learn DNS, HTTP/HTTPS, TCP/IP, TLS, ports, load balancers, reverse proxies, firewalls, NAT, VPNs, CIDR ranges, and subnets. These concepts help you debug failed deployments, slow APIs, blocked traffic, and misconfigured infrastructure.

For example, when a user opens a website, the browser performs DNS resolution, establishes a connection, negotiates TLS, sends an HTTP request, reaches a load balancer or reverse proxy, and finally routes to an application service. DevOps engineers must understand each layer to troubleshoot production issues.

Tools in This Stage

• Free Resource: Use Scaler’s Linux Tutorial.
• Milestone: You are ready to move on when you can create a Bash script that checks disk usage, CPU usage, and service status, then writes the output to a log file.

Stage 2 (Month 3): Scripting with Bash and Python

Scripting comes second because Stage 1 taught you the terminal now you automate it. This is automation scripting, not application development.

DevOps engineers automate repetitive work. That means you must be able to write scripts that interact with servers, APIs, files, cloud services, and CI/CD tools.

What to Learn

Start with Python because it is beginner-friendly, widely used in automation, and supported by cloud SDKs such as Boto3 for AWS. Learn variables, functions, file handling, JSON/YAML parsing, API requests, error handling, and writing reusable scripts.

Also learn Bash because Linux administration depends heavily on shell scripting. Bash is essential for writing deployment scripts, automating log cleanup, checking services, and running commands inside CI/CD pipelines.

After Python and Bash, learn the basics of Go if you want to move deeper into cloud-native engineering. Kubernetes, Docker, Terraform, and many infrastructure tools are written in Go.

Tools in This Step

• Free Resource: Start with Scaler’s Python Tutorial.
• Milestone: You are ready to move on when you can write a Python script that reads a log file, finds error lines, and writes a summary report.

Free Courses by top Scaler instructors

Stage 3 (Month 3–4): Git, GitHub and Branching Workflows

Git comes now because everything after this stage pipelines, Terraform, Kubernetes manifests lives in a repository.

Version control is the foundation of DevOps because modern teams treat everything as code: application code, infrastructure code, Kubernetes manifests, CI/CD pipelines, documentation, and configuration files.

Git Fundamentals

Learn commands such as git init, git add, git commit, git status, git log, git branch, git checkout, git merge, git rebase, git pull, and git push. You should understand how branches work, how merge conflicts happen, and how to resolve them safely.

GitHub and GitLab for DevOps

GitHub and GitLab are more than code hosting platforms. They trigger CI/CD pipelines, enforce branch protection, manage pull requests, store workflow files, and integrate with deployment tools. Learn pull requests, merge requests, code reviews, branch protection rules, issue tracking, and repository secrets.

Branching Strategies

Learn the difference between GitFlow and trunk-based development. GitFlow is structured and common in release-heavy environments. Trunk-based development is faster and common in modern CI/CD teams where developers merge small changes frequently.

Tools in This Step

• Free Resource – Learn Scaler’s Git Tutorial
• Milestone: Create a GitHub repository with branch protection rules, a pull request template, and a basic GitHub Actions workflow.

Stage 4 (Month 4–5): Containers with Docker

Docker comes before cloud on purpose: you can containerise apps locally for free, and everything you deploy to AWS in Stage 6 will ship as a container anyway.

Docker solves the common “works on my machine” problem by packaging an application, runtime, dependencies, and configuration into a portable container image.

Key Docker Concepts

Learn Docker images, containers, layers, volumes, networks, Dockerfiles, Docker Compose, and registries. Understand the difference between an image and a running container. Learn how Docker layers affect image size and build speed.

Dockerfiles and Multi-Stage Builds

A Dockerfile defines how an image is built. Learn instructions such as FROM, WORKDIR, COPY, RUN, ENV, EXPOSE, and CMD. Multi-stage builds help create smaller and more secure production images by separating build dependencies from runtime dependencies.

Docker Compose and Registries

Docker Compose lets you run multi-container applications locally, such as a backend service, database, cache, and message queue. Registries such as Docker Hub and AWS ECR store images that CI/CD pipelines can push and Kubernetes clusters can pull.

Tools in This Step

• Free Resource: Use Scaler’s Docker Tutorial.
• Milestone: Containerize a web application with a database using Docker Compose and push the application image to Docker Hub or AWS ECR.

Stage 5 (Month 5–6): CI/CD Pipelines with GitHub Actions and Jenkins

CI/CD comes after containers because modern pipelines build and ship container images there is nothing to automate until you have an artefact.

CI/CD is the heart of DevOps automation. It helps teams test, build, scan, package, and deploy code automatically whenever changes are made.

CI/CD Concepts

Continuous Integration means every code change is automatically built and tested.

Continuous Delivery means the code is always in a deployable state.

Continuous Deployment means successful changes are automatically released to production.

A typical pipeline includes stages such as linting, unit testing, integration testing, Docker image building, vulnerability scanning, artifact publishing, and deployment.

GitHub Actions, Jenkins, and GitLab CI

GitHub Actions is beginner-friendly because workflows are written in YAML and live inside the same GitHub repository. Jenkins is common in enterprise environments and supports complex pipelines through plugins and declarative pipeline syntax. GitLab CI is strong when teams already use GitLab for source control.

GitOps and ArgoCD: The Modern Deployment Pattern

GitOps treats Git as the single source of truth for infrastructure and application state. Instead of a CI/CD tool pushing changes directly into a Kubernetes cluster, tools like ArgoCD or Flux continuously compare the live cluster state with the desired state stored in Git.

A simple GitOps workflow looks like this: a developer opens a pull request, the team reviews and merges it, ArgoCD detects the change, and then ArgoCD syncs the Kubernetes cluster to match the repository. If someone manually changes the cluster, ArgoCD detects drift and can revert or flag the difference.

Companies prefer GitOps because it improves auditability, rollback safety, environment consistency, and deployment visibility. ArgoCD is also a common DevOps and SRE interview topic in Kubernetes-heavy roles.

Tools in This Step

Milestone: Build a pipeline that runs tests on every pull request, builds a Docker image after merge, scans the image, and pushes it to a registry.

Stage 6 (Month 6–8): Cloud (AWS) and Infrastructure as Code with Terraform

Cloud comes after Docker because your first cloud project is running the container stack you already built — now with real networking, IAM and a bill attached.

Cloud computing allows companies to create servers, databases, storage, networks, and security policies without buying physical hardware. DevOps engineers must know how cloud systems are designed, provisioned, secured, and monitored.

AWS Services to Learn First

Start with AWS

AWS is a strong first cloud provider because it has broad market adoption and a large number of DevOps job requirements. Learn EC2 for virtual servers, S3 for object storage, RDS for managed databases, VPC for networking, IAM for access control, CloudWatch for monitoring, Lambda for serverless functions, and EKS for Kubernetes.

Understand Azure and GCP Too

After AWS basics, learn how the same cloud concepts map to Azure and GCP. Azure is especially common in enterprise and Microsoft-stack companies, while GCP is popular in data-heavy, analytics, and ML-adjacent workloads. Azure DevOps and Google Cloud Build are also common CI/CD tools in enterprise environments.

Cloud Networking and IAM

Cloud networking is a major DevOps interview topic. Learn subnets, route tables, NAT gateways, internet gateways, load balancers, security groups, and private/public networking. Also learn IAM users, roles, policies, least privilege, and temporary credentials.

Free resource: start with a free AWS course before you spend on a personal account.

Terraform and Ansible

Infrastructure as Code (IaC) lets you define cloud resources using configuration files instead of manually clicking through cloud consoles. This makes infrastructure repeatable, reviewable, version-controlled, and easier to audit.

Terraform Fundamentals

Terraform is the most widely used IaC tool. Learn providers, resources, variables, outputs, state files, modules, workspaces, remote state, and state locking. Understand the difference between terraform init, terraform plan, terraform apply, and terraform destroy.

Terraform State and Collaboration

Terraform state tracks the real infrastructure created from your code. In team environments, remote state storage and state locking are critical because two engineers applying changes at the same time can corrupt infrastructure.

Alternatives and Complements

Ansible is often used for configuration management, while Terraform is used for provisioning infrastructure. Pulumi is an IaC alternative that lets engineers define infrastructure using programming languages such as Python, TypeScript, and Go.

Tools in This Stage

Free Resource: Read Scaler’s AWS DevOps guide.

Milestone: Use Terraform to create an AWS VPC, EC2 instance, security group, S3 bucket, and RDS database using reusable modules and connect the EC2 instance to RDS without exposing the database publicly.

Serverless Basics (Optional)

Serverless computing lets you run code without managing servers directly. Cloud providers automatically handle provisioning, scaling, and runtime management.

Serverless Platforms

Common serverless platforms include AWS Lambda, Google Cloud Functions, and Azure Functions. These services run small functions in response to events such as API requests, file uploads, database changes, message queue events, or scheduled timers.

When to Use Serverless

Serverless is useful for lightweight APIs, background jobs, automation tasks, event-driven workflows, and cost-sensitive workloads with unpredictable traffic. Containers are better when you need long-running processes, custom runtimes, complex networking, or more control over infrastructure.

Workflow Orchestration

AWS Step Functions can orchestrate multi-step serverless workflows such as approval systems, ETL pipelines, and automated remediation tasks.

Tools in This Step

Milestone: Build a Lambda function triggered by an S3 upload that processes a file and writes metadata to a database or log stream.

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Stage 7 (Month 8–10): Kubernetes, Monitoring and Production Readiness

Kubernetes comes last among the tools because it orchestrates everything before it: containers from Stage 4, images from your Stage 5 registry, cloud infrastructure from Stage 6. Learn it once the rest is solid and it will actually make sense.

Kubernetes Essentials

Docker is great for running containers on one machine. Kubernetes is used when you need to run containers across clusters of machines with scaling, self-healing, service discovery, and rolling deployments.

Kubernetes Fundamentals

Learn Kubernetes architecture: control plane, worker nodes, pods, deployments, services, replicasets, config maps, secrets, namespaces, ingress, and persistent volumes. Understand how Kubernetes schedules pods, restarts failed containers, and routes traffic to healthy instances.

Helm, Ingress, and Scaling

Helm is the package manager for Kubernetes. It helps you install and manage complex applications using reusable charts. Ingress controllers expose services to external traffic, and Horizontal Pod Autoscaler adjusts pod counts based on load.

Debugging Kubernetes

Learn commands such as kubectl get, kubectl describe, kubectl logs, kubectl exec, and kubectl apply. Practise debugging CrashLoopBackOff, pending pods, image pull errors, failed readiness probes, and resource constraints.

For a deeper path to CKA, follow our Kubernetes roadmap. Free resource: the official Kubernetes tutorials.

Tools in This Step

Milestone: Deploy a containerized app on Kubernetes with deployment, service, ingress, config map, secret, and autoscaling.

Monitoring and Observability (Prometheus, Grafana)

Once software is live, DevOps engineers must monitor performance, detect failures, and help teams respond quickly. Observability answers three questions: what is happening, why it is happening, and how users are affected.

Metrics, Logs, and Traces

Metrics measure system behavior such as CPU usage, memory, latency, request rate, and error rate. Logs provide event-level details from applications and servers. Traces show how a request moves through multiple services in a distributed system.

Prometheus and Grafana

Prometheus collects time-series metrics from applications and infrastructure. Grafana visualizes those metrics in dashboards. Alertmanager can send alerts to Slack, email, PagerDuty, or other incident response tools.

Logging and OpenTelemetry

The ELK Stack or OpenSearch helps teams centralize and search logs. OpenTelemetry is becoming a standard for collecting traces, metrics, and logs across distributed systems.

Tools in This Step

Milestone: Create a dashboard that tracks CPU, memory, request latency, error rate, and uptime for a containerized application.

Security Basics (DevSecOps)

(moved here from old Step 9 security belongs with production readiness, not in an isolated step)

DevOps engineers must understand how to protect infrastructure from unsafe access. Learn least privilege, IAM roles, secret management, SSH hardening, security groups, RBAC, container scanning, dependency scanning, and secure pipeline design. Avoid exposing SSH to the public internet and never store credentials in Git repositories.

Tools in This Step

Milestone: Secure a cloud-hosted application behind Nginx with HTTPS, restricted SSH access, and environment variables stored outside source code.

Beyond the Roadmap: GitOps, Platform Engineering and DevSecOps

The reference table below shows when each advanced area earns its place none of them belong in your first 8 months.

After learning the core roadmap, move into advanced disciplines that separate junior engineers from senior DevOps, SRE, and platform engineering professionals.

Site Reliability Engineering

SRE applies software engineering principles to operations. Learn SLIs, SLOs, SLAs, error budgets, incident response, blameless post-mortems, toil reduction, and automated remediation.

DevSecOps: Integrating Security into the Pipeline

DevSecOps means shifting security earlier into the software delivery process. Instead of waiting until production to find vulnerabilities, teams scan code, dependencies, containers, infrastructure files, and secrets during development and CI/CD.

A practical DevSecOps pipeline may look like this:

Code commit → SAST scan → dependency scan → unit tests → Docker build → Trivy image scan → IaC scan → deploy to staging → DAST scan → production approval

Important DevSecOps tools include Trivy for container vulnerability scanning, SonarQube for static code analysis, OWASP ZAP for dynamic application security testing, HashiCorp Vault for secrets management, and Snyk for dependency and container security.

DevOps engineers do not need to become full-time security analysts, but they must understand secure defaults, least-privilege IAM, secrets management, vulnerability scanning, and RBAC.

AIOps and AI-Assisted DevOps

AIOps uses AI and machine learning to improve operations through anomaly detection, predictive alerting, log analysis, incident correlation, and automated remediation. Tools such as Datadog, Dynatrace, and cloud-native monitoring platforms increasingly include AI-assisted features.

AI also helps DevOps engineers write and review infrastructure code. GitHub Copilot can assist with Terraform, Kubernetes YAML, Bash scripts, and CI/CD workflows, but engineers must still review the output carefully for correctness and security.

In 2026, AI is not replacing DevOps engineers. It is reducing repetitive work and increasing the value of engineers who understand systems deeply enough to validate and improve AI-generated suggestions.

Platform Engineering and Internal Developer Platforms

Platform engineering focuses on building self-service tools for developers. Instead of developers repeatedly asking DevOps teams to provision databases, clusters, pipelines, or environments, platform teams create reusable templates and internal developer portals.

Tools such as Backstage, Terraform, Kubernetes, ArgoCD, Crossplane, and service catalogs are common in platform engineering. This is a strong career path after gaining DevOps and SRE experience.

FinOps and Cloud Cost Optimization

Cloud cost optimization is now a major DevOps responsibility. Learn rightsizing, autoscaling, reserved instances, spot instances, storage lifecycle policies, tagging strategies, and cost dashboards. Engineers who can reduce cloud bills while improving reliability are highly valuable.

Milestone: Build a production-style platform project that includes Kubernetes deployment, Terraform infrastructure, GitOps with ArgoCD, monitoring, alerting, and security scanning.

[Template widget: Free Courses by top Scaler instructors — KEEP; must not parent any stage] [Template widget: Scaler Placement Report and Statistics — KEEP; must not parent any stage]

DevOps Projects to Build at Each Stage

Projects are the best way to turn theory into interview-ready proof. Add these projects to GitHub with clear README files, architecture diagrams, setup steps, screenshots, and lessons learned. Two starter projects from the stage milestones open the list:

Example phrasing only — write your own résumé line from what you actually built. Push every project to GitHub with a README that includes an architecture diagram.

DevOps Tools Every Engineer Should Know in 2026

(kept as a reference under this section)

Month 10–12: DevOps Interview Preparation

This is the stage competitors skip entirely. Four steps:

1. Revise the fundamentals. Linux commands, networking (DNS, TLS, load balancing) and Git internals — interviewers test Stage 1 knowledge hardest because it cannot be faked.

2. Rehearse scenario questions out loud: “A pod is in CrashLoopBackOff — how do you debug it?” (kubectl describe → logs → probes → resources), “The pipeline passes but the deploy fails — where do you look?” (registry auth, manifests, cluster events), “How would you do a zero-downtime deployment?” (rolling updates, blue-green, canary).

3. Practise the hands-on round: writing a Dockerfile, a Terraform module or a GitHub Actions workflow live, with an interviewer watching.

4. Prepare for system-design-lite: sketch CI/CD plus infrastructure for a 3-tier app — repo → Actions → registry → Kubernetes → monitoring → secrets.

Typical interview rounds and how to prepare:

Five questions worth pre-answering:

* Docker image vs container? The image is the immutable template; the container is a running instance of it. * Blue-green vs canary deployment? Blue-green switches all traffic between two identical environments; canary shifts a small percentage of traffic first and grows it. * What is Terraform state? The file mapping your configuration to real infrastructure; remote state plus locking prevents two engineers from corrupting it. * Kubernetes Service types? ClusterIP (cluster-internal), NodePort (port on every node), LoadBalancer (cloud load balancer), with Ingress for HTTP routing in front. * What is an SLO? A target reliability level (for example 99.9% uptime) that your error budget is measured against.
Are You Interview-Ready?

Use this checklist to identify your current level and decide what to learn next.

DevOps Certifications Roadmap

Certifications are not mandatory, but they can strengthen your resume, especially if you are a fresher or switching careers.

Exam prices from certifying-body pages, October 2026. Check the official page before booking.

Recommended Certification Order

For most learners, start with Terraform Associate and CKA. These validate two highly demanded DevOps skills: infrastructure as code and Kubernetes. After that, pursue AWS Certified DevOps Engineer Professional if your target roles are AWS-heavy. If your target companies run on Azure, swap in AZ-400 at the same point.

DevOps Career Scope and Salaries in India (2026)

DevOps is one of the strongest technology career paths in 2026 because almost every modern company needs faster deployments, stable infrastructure, and cloud automation. Startups, product companies, fintech firms, SaaS companies, global capability centres, and large enterprises all hire DevOps engineers to manage delivery pipelines and production infrastructure.

Companies such as SAP, Informatica, Thomson Reuters, HashedIn by Deloitte, Jio, fintech startups, and cloud-native product firms actively hire DevOps, SRE, platform engineering, and cloud infrastructure talent across Indian tech hubs such as Bengaluru, Hyderabad, Pune, Mumbai, Chennai, Gurugram, and Noida.

DevOps Engineer Salary in India by Experience

Source: AmbitionBox and Glassdoor India, October 2026 — re-verify each band before publishing. Pay varies by city, cloud depth, Kubernetes experience and interview performance.

Is ₹21 LPA good for 5 years of DevOps experience? Yes — it sits at the top of the 3–5-year band (₹9–20 LPA) and within the 6–9-year band, so it is at or above market for the experience. Product companies and engineers with strong Kubernetes, AWS and Terraform depth routinely earn more.

US salaries are higher in absolute terms; for authoritative outlook data, see the US Bureau of Labor Statistics software developer outlook.

DevOps vs SRE vs Cloud Engineer vs Platform Engineer

(moved here from before the roadmap — text unchanged)

Many learners searching for a DevOps engineer roadmap are also comparing DevOps with SRE, cloud engineering, and platform engineering. These roles overlap, but their focus areas are different.

Which DevOps Role Should You Target?

Start with DevOps fundamentals first. A strong DevOps foundation naturally opens paths into SRE, cloud engineering, and platform engineering. In 2026, platform engineering is becoming a premium specialization because companies want self-service infrastructure platforms that reduce developer cognitive load.

DevOps in the AI Era: Platform Engineering, MLOps and AIOps

The direction of travel is concrete, not vague:

* Platform engineering: internal developer platforms (Backstage is the reference tool) that give developers self-service infrastructure

* MLOps: the same CI/CD and Kubernetes skills applied to ML workloads, with tooling like Kubeflow and MLflow

* AI-assisted ops: Copilot-style assistants writing Terraform and pipeline YAML, and AIOps platforms correlating alerts before humans wake up

* Policy-as-code: OPA-style guardrails baked into pipelines instead of review checklists

What this means for the roadmap: learn the 7 stages first. AI tools speed up DevOps work, but they don’t replace knowing Linux, networking and Kubernetes. Cloud-native adoption keeps compounding CNCF’s annual surveys have documented year-on-year growth in Kubernetes and cloud-native usage for years (CNCF reports).

DevOps Career Path

(moved under the career H2)

A typical DevOps career path looks like this:

DevOps Intern / Junior Engineer → DevOps Engineer → Senior DevOps / SRE → Platform Engineer / DevOps Architect → Platform Architect / DevOps Manager

Career Progression

Fast-Track DevOps Roadmap for Developers and Sysadmins

Already code or manage servers? Compress the roadmap to 4–6 months by skipping what you know:

* Developer track (≈4–5 months): skip deep Stage 1–2 → Linux ops refresher (2 weeks) → Docker (Stage 4) → CI/CD (Stage 5) → AWS + Terraform (Stage 6) → Kubernetes (Stage 7). Developers usually underestimate Linux and networking depth — don’t skip the Stage 1 networking section. * Sysadmin/support track (≈4–6 months): skip Stage 1 → Python + Git (Stages 2–3) → Docker → CI/CD → Terraform → Kubernetes. Sysadmins usually underestimate Git workflows and coding discipline — force yourself through the PR-and-review habit.

Learning DevOps with Structure and Mentorship

Self-study works, but structure shortens the road. If you’d rather follow a structured DevOps course syllabus than self-study, Scaler’s DevOps, Cloud & AI Platform Engineering program follows a sequence similar to this roadmap: Python → Linux & shell scripting → Git → Docker, Kubernetes, Prometheus and Grafana → CI/CD, GitOps, Ansible and SRE, with AWS, Terraform and MLOps tooling. It runs 12 months, live online, with 1:1 mentorship and five portfolio projects. Before deciding, compare the best DevOps courses in India by format and fee.

Want this roadmap with live classes, mentors and projects? Explore Scaler’s DevOps, Cloud & AI Platform Engineering program →

[Template widget: Scaler Alumni and Their Success Stories — KEEP; the tables that sat under it have moved to their own sections above]

Your Next Step on the DevOps Roadmap

Start Stage 1 this week: spin up a Linux VM and work through the command-line basics. Build the Stage 4 project next a Dockerised app is the portfolio piece that unlocks every stage after it. Revisit the skills checklist monthly, and by month 10 you’ll be preparing for interviews instead of wondering where to begin.

Read These Important Roadmaps: More Paths to Career Success

DevOps Roadmap: FAQs

Can I learn DevOps in 3 months?

You can learn the core tools (Linux, Git, Docker and a CI/CD pipeline) in about 3 months if you already code or manage servers and study 15+ hours a week. To be job-ready which also means cloud, Terraform, Kubernetes and projects most beginners need 9–12 months and developers 4–6 months.

What are the 7 stages of the DevOps lifecycle?

The DevOps lifecycle is commonly described as continuous development, integration, testing, deployment, feedback, monitoring and operations. The roadmap above maps tools to each: Git for development, GitHub Actions or Jenkins for integration and testing, Kubernetes and Argo CD for deployment, and Prometheus and Grafana for monitoring.

Is DevOps highly paid in India?

Yes. DevOps is one of the better-paid engineering tracks in India, and pay rises sharply with Kubernetes, cloud and Terraform skills. Freshers start around ₹5–9 LPA, mid-career engineers commonly make ₹9–20 LPA, and senior DevOps, SRE and platform engineers earn ₹20 LPA and above (AmbitionBox/Glassdoor India, Oct 2026). Check current ranges by city on AmbitionBox.

Is 21 LPA a good salary for 5 years of DevOps experience?

For 5 years of experience, ₹21 LPA is at or above the typical range reported on salary sites for DevOps engineers in India — it tops the 3–5-year band (₹9–20 LPA) in the salary table above. Product companies and engineers with Kubernetes, AWS and Terraform depth often earn more.

How long does it take to become a DevOps engineer?

Following this roadmap at 10–12 hours a week, complete beginners usually take 9–12 months, and developers or sysadmins 4–6 months. The difference comes from fundamentals: Linux, networking and scripting take longest to build from scratch.

Can I become a DevOps engineer without coding?

You don’t need to be an application developer, but you do need to script. Bash and basic Python are enough for most DevOps roles, alongside writing YAML pipelines and Terraform code. Non-coders should spend an extra month on Stage 2 before moving to tools.

What is the difference between DevOps and SRE?

DevOps is a culture and set of practices for building and shipping software faster and more reliably. Site Reliability Engineering (SRE) is a specific role that applies software engineering to operations, using SLOs, error budgets and automation. Many Indian companies use the titles interchangeably, and the roadmap overlaps by about 80%.

How do I build a DevOps portfolio with no experience?

Build 3–4 projects that follow the roadmap stages: a Bash automation script, a Dockerised app with a CI/CD pipeline, Terraform-provisioned AWS infrastructure, and a Kubernetes deployment with Prometheus and Grafana. Put each on GitHub with a README and an architecture diagram, and link them from your résumé and LinkedIn.

Glassdoor