Platform engineer

Learn to build the internal platform other developers deploy on, with a self-service portal and safe defaults, working with AI tools from week 1 and making the platform ready for AI workloads and AI agents, in 24 weeks.

Coming soon · planned, not written yet

Platform engineering ranks fourth among skills India’s global capability centres hire forTaggd, India GCC skills in demand 2026

24
weeks planned
~40 h
a week
6
milestone projects
1
project, grown every week

These are planned figures. The weeks are written after we check the tools against current job postings and official documentation, so the final route may differ.

Built for the AI era

How AI fits this roadmap

AI coding assistants now draft much of the Terraform, Kubernetes YAML and pipeline code a platform team maintains, and developers on your platform will use them too. That moves the job toward setting the rules and checking what ships, so this roadmap teaches you to use an AI assistant every week and to check its output with plans, policies and tests. You also build the platform for AI: a paved road for teams that ship AI services, and safe, limited access for AI agents that act on your infrastructure.

The main project

PaveRoad

A small internal developer platform with a portal, templates, policy checks and a paved road for deploying services, including AI services, plus an AI agent that can only do what you allow.

What you will be able to do

  • 01

    Build a CI/CD pipeline and container workflow that a new service can adopt with a few lines of config, drafting parts with an AI assistant and fixing what it gets wrong.

  • 02

    Define cloud infrastructure as code with Terraform, and review each plan before applying it, including plans an AI assistant drafted.

  • 03

    Run services on Kubernetes with Git-driven, reversible deployments using Argo CD, and roll a bad release back from Git.

  • 04

    Publish a developer portal with Backstage that holds a service catalogue and templates that create new, ready-to-deploy services.

  • 05

    Enforce safe defaults with Kyverno policies, and give an AI agent least-privilege access through MCP with every action logged and reviewable.

01The route

Six months, one month at a time.

Each month ends in a milestone project, so you always have something to show for the work.

  1. Month 1 · Weeks 1–4

    Platform foundations and AI-assisted work

    Linux, Git, scripting, containers and the idea of a platform as a product with developers as its users. You set up an AI coding assistant, read its diffs and test what it writes.

    MilestoneA sample service with scripts and a Dockerfile that build from one command, plus a short user-needs note and a log of AI suggestions you accepted and rejected

  2. Month 2 · Weeks 5–8

    Pipelines and infrastructure as code

    GitHub Actions pipelines that build, test and publish, and Terraform for cloud resources. You use AI to draft both, then review plans line by line and catch config that is too open.

    MilestoneEvery push builds, tests and publishes the service, and its cloud setup is created by Terraform and reviewed by plan

  3. Month 3 · Weeks 9–12

    Kubernetes and GitOps delivery

    Run the service on Kubernetes and deploy from Git with Argo CD. You practise rollbacks and use AI to explain failing pods, then confirm the cause yourself.

    MilestoneAutomated, reversible deployments to a cluster, driven from a Git repository

  4. Month 4 · Weeks 13–16

    Developer portal and self-service

    A Backstage portal with a service catalogue, templates and docs, and Crossplane to offer infrastructure as a simple API. You use AI to draft templates and test them as a new developer would.

    MilestoneA portal where a developer picks a template and gets a new service, pipeline and deployment

  5. Month 5 · Weeks 17–20

    Guardrails, observability and AI workloads

    Kyverno policies for safe defaults, and OpenTelemetry for traces, metrics and logs. You add a paved road for an AI-backed service and watch its errors, slow calls and usage.

    MilestonePolicy checks and dashboards around the platform, including a deployed AI service that follows the paved road

  6. Month 6 · Weeks 21–24

    AI agents, capstone and job search

    An AI agent that works with your platform through MCP, limited to approved actions and fully logged. Then a portfolio write-up, interview preparation and applications.

    MilestoneA working agent with least-privilege access, a portfolio and a job-search pipeline

03What is inside

Everything a week comes with.

Every week is built the same way, so once you have done one you know how all of them work.

  • 01

    One project, PaveRoad

    You grow one internal platform from a single pipeline to a portal with policies and an AI agent, so every tool lands in something real.

  • 02

    A day-by-day plan

    Each week has a Learn part and a Do part, with a checklist so you know when you are done. Most weeks include a task to do with an AI assistant and a step to check its work.

  • 03

    A project in every milestone

    Six milestones, each ending in something you can show and link to.

  • 04

    Reference topics

    Short topic pages with check questions you can come back to.

  • 05

    A job track

    Interview practice, portfolio and resume help, and an application plan.

  • 06

    Progress in your Study plan

    Add weeks to your Study plan, give them dates, and finished days are counted for you.

04Is it for you

Who this is, and is not, for.

Better to know before you start than six weeks in.

It is for you if

  • You can write small programs and have used Linux, Git and a cloud account, and want to build tools for other engineers.
  • You are a developer, DevOps engineer or system administrator moving toward platform work and want to be strong with AI tools.
  • You can give about 40 hours a week for 24 weeks.

It is not for you if

  • You have never written code or used a terminal. Do a beginner course first.
  • You want a job guarantee. Nobody can promise that, and this roadmap does not.
  • You want deep Kubernetes cluster administration or a security career. This roadmap does not go deep on them.
  • You want to let AI change production without review. This roadmap trains you to limit and check it.

?Questions

Before you start

Is this roadmap ready?
Not yet. This is the plan. The weeks are being written and checked against current job postings and official documentation. Leave your email and we will write to you when it opens.
What will it cost?
The price is not set. Roadmaps are bought once, with no subscription. We will announce it when this one opens.
Will the plan change?
It may. Before the weeks are written we check the tools against current job postings, so the lengths and months here are planned, not final.
How is this different from the DevOps roadmap?
DevOps teaches you to ship and run one service. Platform engineering teaches you to build the shared tools, templates and rules that many teams use to ship their own services. Some basics overlap, and the plan builds on them quickly.
Will it get me a job?
It will give you skills and proof of work. It cannot promise a job on a fixed date, because hiring depends on the market, your location and your past work.

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