DevOps and cloud computing are one skill set.

What each half actually is.

Cloud computing is infrastructure you create and destroy through an API instead of buying it. On AWS that means compute such as EC2 and Lambda, storage such as S3 and EBS, networking in the form of a VPC with its subnets, route tables, security groups and load balancers, identity through IAM roles and policies, and managed services such as RDS for databases. The important shift is not that the servers live somewhere else. It is that every one of those things is now created by a call to an API, which means it can be described in a file, reviewed, versioned and rebuilt.

DevOps is what you do with that fact. It is the working practice of getting software from a developer's branch onto that infrastructure repeatedly and safely: source control, a build and test pipeline, container images, an orchestrator that schedules them, infrastructure written as code rather than clicked into a console, monitoring that tells you when it is wrong, and a defined way of responding when it is. Cloud is the noun. DevOps is the verb.

That is why the two job titles blur. In most Indian IT services companies, GCCs and product teams, a listing headed cloud engineer and a listing headed DevOps engineer describe roughly the same day. Someone has to build the VPC, and the same someone has to make sure the pipeline can deploy into it. Splitting that across two people is a handover that nobody wants to manage.

Why either half alone stalls.

The cloud-only candidate has usually passed an associate-level certification and can draw a tidy architecture diagram. They know what an autoscaling group is. What they have never done is build one by any route other than the console, so the first real task, which is almost always to change something that already exists, leaves them with no starting point. They have knowledge of services but no working method.

The DevOps-only candidate has the opposite gap. They have run Jenkins on a laptop and written a Dockerfile, but have never dealt with an IAM role that is missing a single action, cross-account access, or a private subnet that has no route out through a NAT gateway. Their pipeline works until it has to touch a real network, and then they cannot tell whether the failure is theirs or the platform's.

Interviewers know both patterns and probe exactly at the seam between them. A very common question is some version of this: your pod in Kubernetes cannot reach the database, walk me through what you check. A good answer moves through the layers without panic. Is the pod running and healthy, does DNS resolve inside the cluster, does the node's security group allow egress on that port, does the database security group allow ingress from the node group, is the subnet routing correct, is the secret mounted, is the connection string right. Nobody can answer that from one half of the skill set, and that is the whole argument for learning them together.

The stack, in the order teams adopt it.

This order is not arbitrary and it is not a preference. Each layer explains the one above it, and skipping a layer is the single most common reason a self-taught candidate plateaus. Kubernetes is a networking and process problem wearing a YAML costume, so learning it before Linux and TCP/IP means memorising commands you cannot debug.

Notice that the first two rows are not cloud at all. They are the reason experienced system administrators and support engineers convert into these roles faster than fresh graduates who went straight to a container course. The full sequence, module by module, is set out in the DevOps on AWS syllabus.

What you are actually asked to do on day one.

This is the part most course descriptions leave out, and it is the part that decides whether a new hire survives the probation period. You are not handed a blank account and asked to design a platform. You are handed access to a platform someone else built, usually with gaps in its documentation, and asked to change it without breaking anything.

The skill being tested there is reading unfamiliar infrastructure code and changing it safely. That is a genuinely different skill from writing infrastructure from scratch in a tutorial, and it is worth practising deliberately: take an open source Terraform module, change something, and predict the plan output before you run it. If your prediction is wrong, you have found the gap in your understanding.

On-call is part of the job.

Most roles that touch production include a rotation. Typically it is one week in four or six, and on the great majority of nights nothing happens. When something does, the shape of the work is fixed: an alert fires, you acknowledge it, you check whether the thing the alert describes is actually the thing that is broken, you stabilise first and diagnose second, and afterwards there is a review that is meant to be about the system rather than the person.

It is worth being honest with yourself about whether you want this, because it is the trade behind the pay. If the answer is no, the adjacent roles that carry less of it exist: cloud provisioning and migration work, cloud governance and cost management, and pre-sales solution roles. They still need the same underlying knowledge, which is another argument for learning the two halves together rather than betting on one.

Ask about the rotation in the interview. Ask how many people are on it, what the escalation policy is, and what happened at the last serious incident. The answers tell you more about the engineering culture than anything on the careers page.

Where the combined skill set leads.

The titles this opens are cloud engineer, DevOps engineer, site reliability engineer, platform engineer, build and release engineer, and cloud support roles inside global capability centres. Kerala hiring for these sits in IT services companies, in the GCC platform teams that have grown in Thiruvananthapuram and Kochi, and in product startups where one person carries the whole pipeline. Gulf demand for the same profile is steady, and it is one of the few tracks where remote work with an outside employer is genuinely common.

These are market estimates for the role, not Cokonet placement outcomes, and your own offer will depend on employer, location and prior experience. What moves someone up the table is not time served but the scale and messiness of what they have run: a multi-account setup, a migration that could not take downtime, an incident they led. The band by band detail sits in the DevOps on AWS salary guide.

How to learn both without wasting a year.

Follow the layer order above, and resist the temptation to start at the exciting end. Build three things rather than twenty small exercises, because an interviewer can go deep on three and cannot go anywhere useful on twenty. A sensible trio is a highly available three-tier application provisioned entirely with Terraform, a set of containerised services running on Kubernetes with ingress, secrets and health probes configured properly, and a complete pipeline that tests, builds, deploys through stages and can roll back on command with monitoring attached.

On certification, treat AWS Solutions Architect Associate as the shortlisting filter it is in India rather than as proof of ability, and take the Terraform associate credential second. Leave Kubernetes certification until you have actually run clusters, because those exams are practical and reading will not carry you. Everything on this page is what the DevOps on AWS course is sequenced around, and the wider track sits under Cloud, DevOps and Security if you are still deciding between building infrastructure and defending it.

The questions people actually ask.

The pages this guide points to.