DevOpsMASTER PROGRAM

AI-Ready DevOps Master Program

AI can write the code in seconds. Companies hire the engineers who review it, secure it and run it in production. Learn the full DevOps stack, from Linux to Kubernetes, with AI built into every module.

CI/CD & DevSecOpsKubernetes & GitOpsLLMOps & AIOps

View fee & enroll Explore the curriculum

Next live batchThu, 12 Nov, 2026 · 9:00 PM IST Limited seats

Everything you get

Live classes, 24×7 labs and IT 360 Bundle — in one fee

Live: Linux & Python, Docker & CI/CD, AWS & Terraform, Kubernetes & GitOps & LLMOps & AIOps5–6 months on weekdays · 6–7 months on weekendsTaught live by senior engineers — ask your doubt mid-class and get it solved on screen, not in a comment box three days later.Included
24×7 hands-on lab accessReal Linux, Docker and Kubernetes environments — practise at 2 AM if that's when you learn best.Included
Recording of every classMissed a class for work or exams? Watch it the same night and never fall behind the batch.Included
1:1 Zoom resume & career sessionBonusA trainer reviews your resume and maps your next role with you, one to one.Included
IT 360 Bundle · 10 self-paced coursesBonusRHCSA Linux sharpens your DevOps foundation, and the AWS and Azure courses cover the clouds you'll deploy to — in the same fee.
- RHCSA Red Hat Linux
- AWS Solutions Architect
- Azure AZ-104 Administrator
- Cisco CCNA
- Microsoft MCSA
- Office 365
- VMware VCP
- SCCM
- Microsoft Intune
- CompTIA Security+
Included

Direct enrollment

Enroll online. No sales call needed

$999

One-time fee. IT 360 Bundle, 24×7 labs and a resume & career session included.

Enroll now · $999

Enrollment offer: DevOps Master Program. Review the final amount and included access on checkout.

Need help choosing? Request career guidance

Lab running

Your DevOps lab

Git

Docker

Kubernetes

Terraform

Ansible

Monitoring

Registry

LIVE LAB1/5

Launching your DevOps lab…

What is inside the AI-Ready DevOps Master's Program?

AI-Ready DevOps Master's Program is a live, instructor-led program with Live instructor-led · 24x7 labs. It covers 14 modules and 4 guided labs. Try DevOps & Cyber Labs free before you enroll.

Live training: Live instructor-led · 24x7 labs
Curriculum: 14 modules in 6 stages
Linux
Git & GitHub
Python for DevOps, APIs and AI
Ansible
and 10 more in the curriculum
Guided labs: 4 labs
Batch length: 5–6 months on weekdays, 6–7 on weekends
Practice lab: DevOps & Cyber Labs, free trial before you enroll

Try DevOps & Cyber Labs free Request a callback

Finish AI-Ready DevOps Master's Program. Earn a certificate students can verify

We also provide certification after you complete the course. Pass a proctored Network Kings assessment and receive a certificate with a unique credential ID and a public verification record. Students can check it in seconds, and no login is needed.

Issued by Network Kings. The record comes from our system and can’t be edited by the holder.
Live status. If a certificate is ever revoked, its record says so immediately.
Score on record. The assessment score is shown, so the result speaks for itself.

Get or verify your certificate

NETWORK KINGS - Official Digital Credential

Sample credential

Priya Menon

AI-Ready DevOps Master's Program Certification Assessment

Credential ID
Issued: NK-CERT-2026-104729; 14 Aug 2026

Sample credential. Real credentials link to a live verification record.

How to get certified

Prepare, take your assessment, and share your achievement.

Open the exam from your course
Select Get Certification on your course in the Network Kings learning app.
Take the proctored assessment
Answer the timed questions in one sitting with your camera on and the exam in fullscreen.
Pass with 70% or more
The certification team reviews your result and emails your certificate within 24 hours.
Share it anywhere
Add it to LinkedIn or your CV. Anyone can confirm it with the ID or QR code.

LIVE BATCHES · LIMITED SEATS

Upcoming batches for AI-Ready DevOps Master's Program

All upcoming batches

Pick the batch that fits your schedule and enroll online to lock the batch price before the seats run out.

Live batchOffer batch

DevOps Master Program

Thu, 12 Nov, 2026
9:00 PM IST
Weekday classes

Limited seats

Everything you get

Live: Linux & Python, Docker & CI/CD, AWS & Terraform, Kubernetes & GitOps & LLMOps & AIOpsWeekday batch · 5–6 monthsTaught live by senior engineers — ask your doubt mid-class and get it solved on screen, not in a comment box three days later.Included
24×7 hands-on lab accessReal Linux, Docker and Kubernetes environments — practise at 2 AM if that's when you learn best.Included
Recording of every classMissed a class for work or exams? Watch it the same night and never fall behind the batch.Included
1:1 Zoom resume & career sessionBonusA trainer reviews your resume and maps your next role with you, one to one.Included
IT 360 Bundle · 10 self-paced coursesBonusRHCSA Linux sharpens your DevOps foundation, and the AWS and Azure courses cover the clouds you'll deploy to — in the same fee.RHCSA Red Hat LinuxAWS Solutions ArchitectAzure AZ-104 AdministratorCisco CCNAMicrosoft MCSAOffice 365VMware VCPSCCMMicrosoft IntuneCompTIA Security+Included

Try a free lab first Watch a sample class

$999

Bonuses close in

Not ready? Talk to our team Batch details

Join this program, or learn more than one technology

Companies hire engineers who work across networking, cloud, security and automation. Pick how deep you want to go.

DevOps Master Program

₹5,999/ month × 12

Live batches with a mentor
The full roadmap for this field
24x7 labs and the IT 360 Bundle

Join this master program

Best for multi-technology careers

Live Access Pass

₹12,999/ month × 12

Join any of 200+ live batches a year
Every master program, including this one
All Access Pass (50+ recorded courses) and MockInterviewz

See the Live Access Pass

Taking 3 or more master programs? The pass (₹1,55,988 in total) costs less than buying them one by one, and adds the All Access Pass and MockInterviewz.

YOUR PROGRAM ROADMAP

AI-Ready DevOps Roadmap

Become a DevOps Engineer, SRE, Platform Engineer, DevSecOps or LLMOps Engineer. DevOps follows 6 stages: Foundations: Linux · Git · Python · Ansible, Build & ship: Docker · CI/CD & DevSecOps, Cloud & IaC: AWS · Terraform, Run at scale: Kubernetes · GitOps · SRE and more.

₹4 LPA → ₹60+ LPA

WHO CAN LEARN THIS

Fresher or Graduate
System / Network Admin
Developer
DevOps or Cloud Engineer

Automation & scripting
Containers & secure CI/CD
Cloud & Infrastructure as Code
AI deployment & AIOps

Foundations: Linux · Git · Python · Ansible1 lab1/6
Build & ship: Docker · CI/CD & DevSecOps2/6
Cloud & IaC: AWS · Terraform3/6
Run at scale: Kubernetes · GitOps · SRE1 lab4/6
The AI layer: LLMOps · AIOps & Agents1 lab5/6
Capstone: AskOps, end to endcapstone project6/6

Career Roles

4 PATHS

DevOps Engineer
Site Reliability Engineer
Platform Engineer
MLOps / LLMOps Engineer

Salary Potential

15X GROWTH

Junior DevOps (Fresher): ₹4–8 LPA
DevOps Engineer (2–4y): ₹8–20 LPA
Senior DevOps / Platform (4–7y): ₹20–40 LPA
Architect / SRE Lead (7+y): ₹40–60 LPA+

IN THIS PROGRAM

Live instructor-led · 24x7 labs
4 lab exercises
1 capstone project
14 modules in 6 stages
Recording of every class
24×7 DevOps & Cyber Labs

Salary ranges are indicative and vary by city, skills and employer; they are not a guarantee.

View fee & enroll Try DevOps & Cyber Labs free View the full curriculum

Concept illustration of an automated build, test and deployment pipeline.

CONNECT THE CONCEPTS

Explore the systems behind DevOps

The roadmap above shows how the subjects connect. Explore the curriculum below for every topic, lab and project.

EXPERIENCE THE ROLE

See what a DevOps Engineer really does, minute by minute

Watch a real-world incident unfold on the engineer’s screens and in a 3D model of the systems they look after. Then type the commands yourself and try the same task in a lab.

LIVE SHIFT

A release is stuck: the pipeline is red

CI pipeline · main09:20

Build
Test
Push image
Deploy

Run #482 · triggered by a merge to main

✗ Test stage failed after 1m 12s

incident-room

Developer
My release is blocked, the pipeline is red. Can you take a look?

The systems you look after

09:20 · STEP 1 OF 5

The pipeline fails

A developer pushed a new feature, but the automated pipeline failed at the test stage. Nothing can be released until it is green.

You learn this in the program: CI/CD · GitHub Actions / Jenkins

Illustrative shift based on common real-world incidents. Names, times and numbers are examples.

YOUR TURN

Type the commands yourself, and get an honest answer about AI

prod-clusterYour turn: type a command

kubectl get pods -n payments

api-7c9d 0/1 CrashLoopBackOff 6

# Crashing on a loop. Read the logs.

# Your turn. Type a command, or tap one below. Start with: help

Not sure what to type? Tap one:

AI REALITY CHECK · DEVOPS ENGINEER

AI writes boilerplate; reliability stays human

AI is already good at

Writes pipeline, Dockerfile and Terraform boilerplate
Explains errors and suggests fixes
Generates tests and documentation

You stay essential for

Keeps production reliable and recovers when it fails
Designs platforms, cost and security guardrails
Reviews AI-written code and configuration before it ships
Owns on-call and the postmortem

Skill to add: Go deep on Kubernetes, observability and Terraform, and learn to review AI-generated infrastructure code.

GitHub Actions
Jenkins
Docker
Kubernetes
Terraform
Grafana

Our assessment, not a guarantee. No job is fully AI-proof: people who use AI well replace people who do not.

3-MINUTE CHALLENGE

A deploy just broke production. Make five calls like the on-call engineer.

Score yourself, get a badge, and see why each answer is right or wrong.

Start the challenge

DO THIS TASK YOURSELF

Open the DevOps & Cyber Labs trial and work through the same kind of task

Real systems, guided steps. The lab site opens only when you click.

Open the DevOps & Cyber Labs trial

READY WHEN YOU ARE

Next live batch: Thu, 12 Nov, 2026 · 9:00 PM IST

Review the fee and your enrollment offer before you pay.

Enroll now See batch details

YOUR PROGRAM AT A GLANCE

Be the engineer who checks and owns the result.

Live training: Live instructor-led · 24x7 labs
Curriculum: 14 modules in 6 stages
Learning approach: Classes + practical work

Included in your learning experience

Live instructor-led training by industry practitioners
AI in every module, from Linux to Kubernetes
24x7 hands-on DevOps labs
Troubleshooting labs: we break things, you fix them
Deploy an LLM app on Kubernetes and monitor its cost and quality
Build an AI ops agent that fixes alerts after human approval
AskOps production capstone to demo in any interview
A portfolio repo for every module, plus mock interviews

WHY THIS PROGRAM

DevOps for the AI era

AI can now write a Dockerfile, a Terraform module or an Ansible playbook in seconds. It cannot decide whether that code is safe, debug it at 2 a.m. when production is down, or take responsibility for the outcome. Those are the engineers companies are hiring.

You use AI the way working engineers do: let AI write the first draft, then you review it, test it, secure it and run it in production.

What makes it different

AI in every module, not a bolt-onEach module covers the core skill first, then how to do it faster and better with AI assistants.
Reviewing AI output is a graded skillYou keep a log of real mistakes you caught in AI-generated code. Employers value this judgment above anything else.
You deploy AI, not just use itYou run an LLM-powered app on Kubernetes and monitor its speed, cost and answer quality.
You build an AI ops agentIt reads alerts, suggests a fix and waits for a human to approve it. This is where operations work is heading.
Troubleshooting labsWe break things on purpose and you fix them. Debugging is the skill AI replaces least.
One real capstoneA complete production system you can demo in any interview.

FIND YOUR STARTING POINT

Is this program for you?

You are	Your path through the program	You leave with
Fresher or graduate	Every module from Linux upward, with extra troubleshooting labs	Job-ready DevOps skills, a GitHub portfolio and a capstone to demo
System or network admin, support engineer	Quick pass through Linux; more depth on automation, cloud and Kubernetes	A move from running servers by hand to automating them
Developer	Quick pass through Git and Python; more depth on CI/CD, containers and cloud	The ability to ship and run your own code in production
Working DevOps or cloud engineer	Faster pace through Modules 1–7; focus on GitOps, SRE, AI deployment and AIOps	The AI-era skills newer roles are starting to ask for

Before you start: Basic computer skills and a laptop with at least 8 GB RAM. No coding experience is needed; we teach Python from the start.

Discuss your current skills with an advisor to choose the right starting point and batch.

Still choosing? Find your learning path

EXPLORE THE FULL SYLLABUS

From foundations to applied skills

Follow the complete curriculum, including the topics, lab exercises and project work in each section.

Stage 1

Foundations

The skills every DevOps engineer uses daily, learned alongside the AI tools that make them faster.

Linux

Command line, scripting, system admin. AI angle: use AI to write and explain shell scripts, then verify them.

What you'll learn

Filesystem, permissions, users and groups, processes, package management
Networking basics: IP, ports, DNS, SSH, curl, firewalls
Services and logs with systemd and journalctl
Bash scripting: variables, loops, functions, cron jobs
Troubleshooting a slow or broken server: CPU, memory, disk, logs

The AI edge

Use an AI assistant to explain unfamiliar commands and error messages
Generate scripts with AI, then test them and spot unsafe commands before you run them
Use AI terminal assistants safely: what to share and what never to paste

Git & GitHub

Version control, teamwork, code review. AI angle: AI-assisted reviews and commit messages; GitHub as your portfolio.

What you'll learn

Commits, branches, merge vs rebase, resolving conflicts
Pull requests, code review, branch protection
Team workflows: trunk-based development and conventional commits
GitHub Issues and Projects for planning work

The AI edge

AI-generated commit messages and PR summaries, and when to rewrite them
AI code review assistants: what they catch and what they miss
Building a GitHub profile recruiters can browse

Python for DevOps, APIs and AI

Automation, REST APIs, Flask. AI angle: call LLM APIs from your own Python code.

What you'll learn

Python basics: data types, functions, modules, error handling, virtual environments
Working with files, JSON and YAML
Calling REST APIs with requests; automating cloud and tool APIs
Building a small REST API with Flask; testing with pytest
Calling LLM APIs: prompts, structured JSON output, rate limits and token cost

The AI edge

Write your first AI-powered tool: a script that reads log files and summarizes errors in plain English
Learn how LLM APIs work under the hood, so the AI later in the course isn't a black box

Hands-on

A Flask API plus an AI log summarizer, both tested and committed to GitHub

Ansible

Configuration management at scale. AI angle: have AI draft roles, then find and fix what it got wrong.

What you'll learn

Inventories, ad-hoc commands, playbooks, variables, handlers
Roles and collections; Jinja2 templates
Idempotency: why running a playbook twice must change nothing the second time
Ansible Vault for secrets; managing Linux and Windows hosts
Introduction to Ansible Automation Platform (AAP)

The AI edge

Let AI draft a role, then find the bugs: non-idempotent tasks, hard-coded secrets, invented module options
Use ansible-lint and tests as the final check on AI-written automation
Preview: in Module 13, Ansible becomes the "hands" of an AI ops agent

Stage 2

Build and ship

Package applications once, run them anywhere and deliver every change securely.

Docker & Microservices

Containers, Compose, service design. AI angle: containerize an AI chatbot with a vector database.

What you'll learn

Monolith vs microservices: when each makes sense, and the trade-offs
Docker images, containers, volumes and networks
Writing good Dockerfiles: multi-stage builds, small images, non-root users
Docker Compose for running multi-service apps locally
Container registries and image tagging

The AI edge

Containerize a real AI chatbot: a Python API, a vector database and a front end
Review AI-generated Dockerfiles for common mistakes: running as root, bloated images, secrets baked in

CI/CD & DevSecOps

GitHub Actions, Jenkins, security scans. AI angle: pipelines that check AI-written code like any other code.

What you'll learn

CI/CD concepts: build, test, release, deploy
GitHub Actions in depth: workflows, jobs, matrices, reusable workflows
Jenkins: pipelines as code with a Jenkinsfile, agents, plugins
Security in the pipeline: secret scanning (Gitleaks), image scanning (Trivy), SBOMs, image signing (Cosign)
Keyless cloud access with OIDC, so no passwords or keys sit in your pipeline

The AI edge

Treat AI-written code like any other code: it must pass the same tests and scans
Use AI to explain failing pipeline logs and suggest fixes, then confirm them yourself
Test AI features in CI with a mock model, so tests stay fast and free

Stage 3

Cloud and infrastructure as code

Build real AWS environments, then create and destroy them entirely from code.

AWS for DevOps

Core cloud services, IAM, cost control. AI angle: managing the cost of AI and GPU workloads.

What you'll learn

Core services: EC2, VPC networking, S3, RDS, Elastic Load Balancing, Auto Scaling
Identity and security: IAM users, roles and policies; least privilege; Secrets Manager
Containers on AWS: ECR, ECS with Fargate, and an introduction to EKS
AWS developer tools: CodePipeline and CodeBuild; monitoring with CloudWatch
Cost control (FinOps): budgets, alerts, right-sizing, spot instances

The AI edge

Why AI workloads are expensive (GPUs, tokens, storage) and how teams keep the bill under control
A tour of AWS's AI services, such as Amazon Bedrock, and where DevOps engineers fit in
Use AI assistants to read and explain IAM policies, then check them against least privilege yourself

Terraform

Infrastructure as code. AI angle: AI-drafted infrastructure, caught by policy checks.

What you'll learn

Providers, resources, variables, outputs, data sources
State: remote state in S3, locking, and why it matters for teams
Modules and separate environments (dev and prod) from the same code
Plan and apply workflows in CI, with the plan posted for review
Policy and quality checks: tflint, Checkov, and an introduction to policy as code

The AI edge

Have AI draft infrastructure, then catch what it gets wrong: open security groups, public buckets, missing encryption
Let automated policy checks act as a safety net for both humans and AI

Stage 4

Run at scale

Run production systems that scale, recover on their own and tell you when something is wrong.

Kubernetes

Deploying and scaling containers. AI angle: running AI workloads and autoscaling them.

What you'll learn

Architecture: control plane, nodes, pods
Deployments, Services, Ingress, ConfigMaps and Secrets
Health probes, resource requests and limits, autoscaling (HPA)
Storage, namespaces, RBAC and network policies
Packaging apps with Helm; running on Amazon EKS, plus local clusters (kind or k3d)

The AI edge

Use AI to explain kubectl output and debug failing pods: CrashLoopBackOff, Pending, OOMKilled
How AI workloads differ: heavier memory needs, long-running requests, GPU scheduling

Hands-on

Deploy the AI chatbot to Kubernetes with autoscaling, then survive a node failure without downtime

GitOps with Argo CDNew

Argo CD, Git as the single source of truth. AI angle: safe, reviewable change flow for AI agents too.

What you'll learn

GitOps principles: Git as the single source of truth for what runs in the cluster
Argo CD: applications, sync, self-healing, drift detection
Promoting releases from dev to prod through pull requests
Rolling back a bad release by reverting one commit
Admission policies with Kyverno: allow only signed images, block root containers

The AI edge

GitOps is the safest way to let automation, including AI agents, change production: every change is a reviewable pull request with a full history

Observability & SRENew

Metrics, logs, traces, SLOs, incidents. AI angle: monitoring LLM latency, token cost and answer quality.

What you'll learn

The three signals: metrics (Prometheus), logs (Loki), traces (OpenTelemetry)
Dashboards in Grafana; alerts with Alertmanager
SRE practice: SLIs, SLOs and error budgets; alerting on what users feel
Incident response: on-call, runbooks, and blameless postmortems

The AI edge

Monitoring AI apps: response latency, tokens used, cost per request, rate-limit errors and "no answer" rate
Use AI to summarize noisy logs during an incident, while you keep the final call

Stage 5

The AI layer

The part most DevOps courses skip: running AI in production, and using AI to run production.

Deploying AI Apps (LLMOps)New

LLMOps: serving and operating AI apps. AI angle: RAG apps, model serving, GPU nodes.

What you'll learn

How an AI app works: LLMs, embeddings, vector databases and RAG, explained for engineers
Hosted models through an API vs self-hosted open models
Serving models on Kubernetes: GPU node pools, inference servers (vLLM, Ollama), autoscaling on load
Operating AI apps: rate limits, retries, caching, prompt and model versioning, API keys
Testing AI apps: a fixed question set scored on every change
AI security basics: prompt injection, data leakage, guardrails

Hands-on

Deploy a RAG chatbot that answers questions from your own documents, then compare a hosted model against a self-hosted one on speed and cost

AIOps & AI AgentsNew

Automated operations. AI angle: agents that diagnose alerts and fix them after human approval.

What you'll learn

What AIOps is: using AI to detect, diagnose and fix operational problems
How AI agents work: an LLM plus tools plus a loop
Giving agents tools through MCP (Model Context Protocol): read metrics, logs, query Kubernetes
Building an alert-triage agent: gathers evidence, writes a diagnosis, proposes a fix
Guardrails: read-only by default, allow-list of actions, human approval, full audit log
Executing approved fixes through Ansible runbooks or GitOps pull requests; when not to trust the agent

Hands-on

Build an ops agent that receives a real alert, investigates it, and opens a ticket with its diagnosis and an approve button. The fix runs only after a human says yes

Stage 6

Capstone

Ship a complete AI app to production the way a real platform team would.

Capstone: AskOps

Everything, end to end. AI angle: ship an AI app to production with an AI ops agent.

What you'll learn

In teams, you build AskOps, an AI assistant that answers questions from company documents, and run it in production the way a real platform team would

APPLY WHAT YOU LEARN

What you build

The AI app: three Python microservices, containerized and tested
CI pipelines with security scanning and signed images
The full AWS environment in Terraform, with servers configured by Ansible
Deployment to Kubernetes through Argo CD
Dashboards, SLOs and alerts, including AI cost and quality metrics
An AI ops agent that diagnoses alerts and fixes them after human approval

LEARNING THAT YOU CAN APPLY

Turn the syllabus into practical work

Work through the exercises with your instructor, test configurations and explain the decisions behind your solution.

Python for DevOps, APIs and AI1 practical exercises Kubernetes1 practical exercises Deploying AI Apps (LLMOps)1 practical exercises AIOps & AI Agents1 practical exercises Capstone: AskOps0 practical exercises · 1 project

Skills you’ll work toward

A production-grade capstone on GitHub to demo in any interview
A portfolio repo for every module
Hands-on experience with the tools on current DevOps job descriptions: Linux, Git, Python, Ansible, Docker, GitHub Actions, Jenkins, AWS, Terraform, Kubernetes, Argo CD, Prometheus, Grafana
Practical AI skills: LLM APIs, RAG, LLMOps and AI agents
The habit employers value most: using AI to work faster while staying the engineer who checks and owns the result

PLAN THE NEXT STEP

Build on your program

Explore the supporting bundles and career services available with this program. Your selected enrollment offer sets the final inclusions.

DevOps Self-Paced Learning BundleIncluded learning bundle

Live sessions move fast. This bundle lets you revisit every DevOps tool at your own pace, for revision or interview prep. Recorded by DevOps practitioners, available anytime on any device.

Recorded · Anytime
Linux · Git · Python · Ansible · Docker
CI/CD · AWS · Terraform · Kubernetes · GitOps
Observability · LLMOps · AIOps & AI Agents

IT 360 Self-Paced Bundle · 10 coursesIncluded learning bundle

RHCSA Linux sharpens your DevOps foundation, and the AWS and Azure courses cover the clouds you'll deploy to. Self-paced video on the Network Kings learning platform, studied alongside your live classes.

RHCSA Red Hat Linux
AWS Solutions Architect
Azure AZ-104 Administrator
Cisco CCNA
Microsoft MCSA
VMware VCP
Office 365
SCCM
Microsoft Intune
CompTIA Security+

Engineer Edge Placement ProgramCareer support add-on

Engineer Edge specialises in placing DevOps and Cloud engineers in top companies. They actively apply for jobs on your behalf and support you until you land the offer.

Hiring Partner
Resume Building · LinkedIn Optimization
Mock Interviews · Technical Interview Prep
Job Application Support · Placement Assistance

How the learning areas connect

Explore the learning areas in this path. Check the curriculum above for the subjects included in your program.

Stage 1 · FoundationsLinux · Git · Python · Ansible
Stage 2 · Build and shipDocker · CI/CD & DevSecOps
Stage 3 · Cloud and IaCAWS · Terraform
Stage 4 · Run at scaleKubernetes · GitOps · SRE
Stage 5 · The AI layerLLMOps · AIOps & Agents
Stage 6 · CapstoneAskOps, end to end

Career directions to explore

Roles depend on your experience, practical skills and the opportunities you pursue.

DevOps PathJunior DevOps Engineer → DevOps Engineer → Senior DevOps Engineer → DevOps Architect
Platform PathCloud / DevOps Engineer → Platform Engineer → Site Reliability Engineer → Infrastructure Architect
DevSecOps PathDevOps Engineer → Cloud DevOps Engineer → DevSecOps Engineer → Security / Cloud Architect
Roles this program prepares you forDevOps Engineer · Cloud Engineer (AWS) · Site Reliability Engineer (SRE) · Platform Engineer · DevSecOps Engineer · MLOps / LLMOps Engineer (entry level)

Salary potential in India

Indicative ranges by role and experience, not a guarantee. Your offer depends on your skills, experience and interviews.

Role & experience level	Salary range
Junior DevOps / Cloud Support Engineer (Fresher)	₹4–8 LPA
DevOps Engineer / Cloud DevOps Engineer (2–4 yrs)	₹8–20 LPA
Senior DevOps / Platform Engineer (4–7 yrs)	₹20–40 LPA
DevOps Architect / SRE Lead (7+ yrs)	₹40–60 LPA+

BEFORE YOU GET STARTED

Your questions, answered

What does the AI-Ready DevOps Master's Program cover?

14 modules in six stages: Linux, Git, Python and Ansible; Docker and CI/CD with DevSecOps; AWS and Terraform; Kubernetes, GitOps with Argo CD and Observability & SRE; then the AI layer (LLMOps and AIOps & AI agents) and the AskOps production capstone. Every module pairs a core DevOps skill with a practical AI angle.

What does "AI-ready" mean in this program?

You learn each core skill first, then how to do it faster with AI assistants, and how to check what AI gets wrong. You also deploy an LLM app on Kubernetes, monitor its latency, token cost and answer quality, and build an AI ops agent that diagnoses alerts and fixes them only after a human approves.

Do I need coding experience?

No. You need basic computer skills and a laptop with at least 8 GB RAM. Python is taught from the start in Module 3.

Who is this program for?

Freshers and graduates, system or network admins and support engineers, developers, and working DevOps or cloud engineers. Each group takes a different path through the modules: freshers start from Linux with extra troubleshooting labs, while working engineers move faster through Modules 1–7 and focus on GitOps, SRE, AI deployment and AIOps.

Which tools will I use?

Linux, Git and GitHub, Python and Flask, Ansible, Docker and Compose, GitHub Actions, Jenkins, Gitleaks, Trivy, Cosign, AWS (EC2, VPC, S3, IAM, ECS, EKS, Bedrock), Terraform, Kubernetes and Helm, Argo CD, Kyverno, Prometheus, Loki, OpenTelemetry, Grafana, vLLM, Ollama and MCP.

Will I get hands-on labs?

Yes. You get 24x7 DevOps & Cyber Labs (labs.nwkings.com), troubleshooting labs where things are broken on purpose for you to fix, and a hands-on build in most modules.

What is the capstone project?

In teams you build AskOps, an AI assistant that answers questions from company documents: three Python microservices, secure CI pipelines, AWS built in Terraform, deployment to Kubernetes through Argo CD, dashboards and SLOs, and an AI ops agent. It goes on GitHub for you to demo in interviews.

What jobs can I apply for?

DevOps Engineer, Cloud Engineer (AWS), Site Reliability Engineer, Platform Engineer, DevSecOps Engineer and entry-level MLOps / LLMOps Engineer roles. Roles depend on your experience, practical skills and interview performance.

Where can I check the fee and enroll?

Use the Enroll button on this page to see the current offer and pay securely on nwkings.com: full payment or no-cost EMI in India, USD for international learners. Prefer to talk first? Request a callback and a course advisor will confirm the batch, inclusions and fee.

ENROLLMENT & FEES

Make your next step clear

Review your learning plan and enrollment details, or ask the team to help with your questions.

Curriculum and prerequisites
Batch timings and learning format
Fees and included access

Enrollment offer: DevOps Master Program

View fee & enroll Request a callback Compare learning plans

Review the final amount and your selected enrollment offer before paying.

Already enrolled? Go to student login ↗

Connect the concepts.
Put them to work

Move from understanding a system to configuring, testing and improving it through guided practice.

Configure a network
Trace connections and practice working with devices and services.
Test what changes
Explore lab scenarios and inspect how configuration choices affect a system.
Build repeatable workflows
Explore Linux, scripting and automation in the relevant learning paths.

Explore the lab environments

Confirm the available labs and access period for your chosen course.

Illustration of a miniature switch, servers, cloud, security gateway and delivery modules connected in an infrastructure learning lab. — A connected view of networking, cloud, security and automation.

Learn from top 1% engineers.

Meet instructors who bring networking, cloud and automation experience into the classroom.

Founder · Networking

Atul Sharma

12+ years in networking

Networking experience spanning Aricent, TCS, Apple and Juniper.

AWS cloud

Sanjay Sharma

20+ years of experience

Cloud mentor bringing extensive hands-on AWS experience to the classroom.

Linux & DevOps

Amit Bhatt

11+ years of experience

DevOps automation experience at CDAC, IBM and Integrated Solutions.

Industry experience. Practical explanations. A human connection.

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Success stories

Tushar Hajarnis

Network L1

“Always thankful to Network Kings for their guidance and support.”

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Aadil Hameed dar

Network Engineer L1

“Industry-expert trainers made networking easy to understand.”

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View fee & enroll Enrollment offer: DevOps Master Program

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AI-Ready DevOps Master Program

Live classes, 24×7 labs and IT 360 Bundle — in one fee

Enroll online. No sales call needed

What is inside the AI-Ready DevOps Master's Program?

Finish AI-Ready DevOps Master's Program. Earn a certificate students can verify

How to get certified

Open the exam from your course

Take the proctored assessment

Pass with 70% or more

Share it anywhere

Upcoming batches for AI-Ready DevOps Master's Program

DevOps Master Program

Join this program, or learn more than one technology

DevOps Master Program

Live Access Pass

AI-Ready DevOps Roadmap

Explore the systems behind DevOps

A release is stuck: the pipeline is red

The pipeline fails

Type the commands yourself, and get an honest answer about AI

AI is already good at

You stay essential for

A deploy just broke production. Make five calls like the on-call engineer.

Open the DevOps & Cyber Labs trial and work through the same kind of task

Be the engineer who checks and owns the result.

Included in your learning experience

DevOps for the AI era

What makes it different

Is this program for you?

From foundations to applied skills

What you'll learn

The AI edge

What you'll learn

The AI edge

What you'll learn

The AI edge

Hands-on

What you'll learn

The AI edge

What you'll learn

The AI edge

What you'll learn

The AI edge

What you'll learn

The AI edge

What you'll learn

The AI edge

What you'll learn

The AI edge

Hands-on

What you'll learn

The AI edge

What you'll learn

The AI edge

What you'll learn

Hands-on

What you'll learn

Hands-on

What you'll learn

What you build

Turn the syllabus into practical work

Skills you’ll work toward

Build on your program

How the learning areas connect

Career directions to explore

Salary potential in India

Your questions, answered

Connect the concepts. Put them to work

Configure a network

Test what changes

Build repeatable workflows

Learn from top 1% engineers.

Atul Sharma

Sanjay Sharma

Amit Bhatt

Success stories

Tushar Hajarnis

Aadil Hameed dar

Explore, talk it through, or enroll

Find the right direction

Connect the concepts.
Put them to work