Start with Linux, networking and Git basics, then learn shell scripting and Python before moving into AWS core services such as EC2, VPC, S3 and IAM. From there, build skills in CI/CD tools like Jenkins, containers with Docker, orchestration with Kubernetes, and infrastructure as code with Terraform and Ansible. Practice by deploying real projects end to end rather than only watching theory videos, and validate your skills with an AWS certification. Following a structured roadmap and getting guidance from someone who works on production DevOps daily can save you months of confusion.

An AWS DevOps engineer designs, automates and maintains cloud infrastructure and release pipelines on the AWS platform. Day-to-day work includes writing Terraform templates, managing Kubernetes or EKS clusters, building CI/CD pipelines, automating tasks with shell or Python scripts, setting up monitoring with CloudWatch, and responding to production incidents. The role sits between development and operations, so companies expect both coding ability and deep AWS knowledge.

Salaries vary by city, company and hands-on skill depth, but as a broad range, freshers typically earn around ₹4–8 LPA, engineers with 3–5 years of experience often fall in the ₹12–20 LPA band, and senior engineers with strong Kubernetes, Terraform and AWS architecture skills can cross ₹25–40 LPA. Candidates with real project experience, certifications and strong interview performance consistently negotiate better offers than those with only theoretical knowledge.

The recommended path is to first clear an associate-level exam such as AWS Solutions Architect Associate or Developer Associate, then attempt the AWS DevOps professional certification, officially called AWS Certified DevOps Engineer – Professional. Build solid hands-on experience with CI/CD pipelines, Terraform or CloudFormation, monitoring and incident automation before attempting the professional exam, since the questions are heavily scenario-based. Combine video lessons, hands-on labs and practice tests, and revise the key exam domains like deployment automation, security and self-healing infrastructure.

The most common questions cover the difference between IAM roles and users, EC2 and EKS fundamentals, VPC and networking design, building a CI/CD pipeline from scratch, blue-green versus rolling deployments, Terraform versus CloudFormation, and troubleshooting Kubernetes issues like CrashLoopBackOff. At mid and senior levels, expect scenario-based questions such as designing a zero-downtime deployment on AWS or recovering from a failed production release. Interviewers increasingly test how you think through trade-offs, not just tool definitions.

Freshers are usually asked about basic Linux commands, Git concepts like merge versus rebase, what CI/CD means, the difference between a Docker image and a container, why Kubernetes is used, and how a simple Jenkins pipeline works. Some interviewers also check basic shell scripting and awareness of Agile and Scrum. The answers that stand out are the ones backed by a self-learned project, so build a small end-to-end deployment and be ready to explain every tool you used.

At this level, interviews go deep into scenario-based questions: designing a zero-downtime deployment, debugging a pod that keeps crashing in production, managing Terraform state across a team, securing secrets in pipelines, and optimizing cloud costs. Expect detailed grilling on the projects on your resume, including why you chose a particular tool and what broke in production. Practising out loud through mock interviews and being able to whiteboard an architecture are what usually separate selected candidates from the rest.

Most interviewers focus on Azure Pipelines, YAML pipelines versus classic editors, agent pools and self-hosted agents, service connections, variable groups, handling secrets securely, and setting up CI/CD for a containerized application. You may also be asked how Azure DevOps compares with Jenkins or GitHub Actions, and how to configure approvals and gates for production deployments. Hands-on practice inside a free Azure DevOps organization makes these answers far easier.

The best starting point is a hands-on, structured path that covers pods, deployments, services, ConfigMaps, Secrets and scaling, practised on minikube, kind or a cloud sandbox instead of theory alone. Once the basics are clear, move to real-time project videos that show how Kubernetes is actually used in companies, because that production context is what interviews and jobs demand. Avoid hopping between multiple tutorials; finish one complete course, deploy a real application, and only then explore advanced topics like Helm and Ingress.

Docker is a containerization platform that packages an application with all its dependencies into a container so it runs consistently on any machine, removing the classic "works on my system" problem. Kubernetes is a container orchestration platform that manages those containers at scale, handling scheduling, auto-scaling, self-healing, rolling updates and service discovery across a cluster. In simple terms, Docker builds and runs containers, while Kubernetes runs and manages them reliably in production, which is why the two are almost always learned together.

Yes, Kubernetes is open source, governed by the CNCF, and completely free to use commercially, including for business and production workloads. What you pay for is the underlying infrastructure such as servers or cloud VMs, and managed Kubernetes services like EKS, AKS or GKE charge their own control-plane fees. Some enterprise distributions and add-on tools built on top of Kubernetes carry separate licenses, but the core platform itself is free.

AWS DevOps Guru is a machine-learning-powered operations service that automatically detects anomalies in your AWS applications by analyzing metrics, logs and traces. Instead of manually digging through dashboards, it surfaces abnormal behaviour, highlights the likely root cause and suggests remediation steps, which reduces mean time to resolution. It is a practical example of AIOps and increasingly shows up in DevOps interview discussions as well.

AWS DevOps Agent is a newer agentic AI offering from AWS that autonomously investigates operational issues in your environment, correlating alarms, logs, metrics and traces to produce a root-cause analysis with recommended fixes. For teams exploring how to use AWS DevOps Agent, the typical flow is to enable it for your account, grant it access to your environment's telemetry, and then ask it in plain language to investigate an alert or incident. It is aimed at cutting down the manual triage work on-call engineers usually do during outages.

Look for a course that teaches through real-time projects rather than slides, covers the complete toolchain including Linux, Git, Jenkins, Docker, Kubernetes, Terraform, Ansible and at least one cloud platform, and includes doubt support and mock interview preparation. Programs that show actual day-to-day client tasks and production scenarios prepare you far better than theory-only courses because interviews test practical judgement. Before paying, preview the teaching style and check whether the curriculum includes hands-on labs you can showcase on your resume.