Master Docker, Kubernetes, Terraform and AWS — and learn what most institutes skip: using GitHub Copilot, ChatGPT and Claude to write pipelines and infrastructure code, deploying LLM applications, and AIOps. Live classes, max 10 students per batch, taught by an AWS-certified ex-TCS DevOps Lead.
DevOps job descriptions have changed. Alongside Kubernetes and Terraform, companies now list AI-assisted automation, LLM deployment experience and AIOps. Engineers who combine both stacks interview better and negotiate higher.
Pipelines, Dockerfiles, Terraform modules — teams expect engineers to produce them faster with Copilot and ChatGPT, and to catch the mistakes AI makes. We teach both the speed and the guardrails.
Someone has to deploy and operate them — GPUs, vector databases, token costs, scaling. That someone is a DevOps engineer with GenAI skills. You will deploy a real LLM application as your capstone.
Puppet, Nagios and Chef still dominate traditional syllabi. A GenAI-era portfolio makes your profile stand out immediately — in resume screens and in interviews.
Classic DevOps depth with a GenAI layer woven through every phase — not a token “AI tools” lecture at the end.
Tools covered: Linux · Git · Docker · Kubernetes · Jenkins · GitHub Actions · Terraform · Ansible · AWS · Prometheus · Grafana · GitHub Copilot · ChatGPT · Claude · Helm · ArgoCD
| What you learn | Traditional institute | This course |
|---|---|---|
| Core stack | Often includes legacy tools (Puppet, Nagios, Chef) | 2026 stack: Kubernetes, Terraform, GitOps, AWS |
| AI-assisted engineering | Not covered, or one theory session | Copilot/ChatGPT used hands-on from week 1 |
| LLM app deployment | Not covered | Full capstone project on Kubernetes + AWS |
| AIOps / monitoring | Nagios-era monitoring | Prometheus + Grafana with AI-driven incident workflows |
| Batch size | 30–100+ students | Maximum 10 students per batch |
| Fees | Hidden behind “Get Fees” forms | Public: ₹15,000 / ₹35,000 / ₹70,000 — full fee breakdown |
Graduate with an AI-era DevOps portfolio instead of a generic certificate. Entry roles: 4–8 LPA.
Your process knowledge transfers directly. Add automation + GenAI and move to 8–18 LPA DevOps roles.
Add the GenAI layer — LLM deployment, AIOps, AI-assisted IaC — that new job descriptions now list.
Structured path from zero. See our detailed guide on switching to DevOps from a non-IT background.
Coming from a non-technical background? Read our complete non-IT to DevOps career-switch guide.
Lead DevOps Instructor · AWS Certified Solutions Architect · 8+ years DevOps, ex-DevOps Lead at TCS
Every cohort is taught live by Firoz — not a rotating panel of junior trainers. He spent 8+ years as a DevOps and cloud engineer, including a DevOps Lead role at TCS, shipping production CI/CD, Kubernetes and Terraform infrastructure before moving into full-time teaching in 2021. Because batches are capped at 10, he knows where each student is stuck by the second week.
He keeps the curriculum current against what hiring managers actually ask in interviews, and runs the GenAI module himself rather than outsourcing it.
Transparent pricing with no hidden fees — pick the level that matches your goals
Beginners · 40 hours · Weekend batches (Sat + Sun)
Entry-level DevOps roles (4-8 LPA)
Most popular · 50 hours · EMI available · Weekend batches
DevOps Engineer (8-18 LPA)
Expert level · 150 hours · Full mentorship + placement
Senior DevOps/SRE Engineer (18-40+ LPA)
Real stories from real alumni — watch their journey
It is a DevOps course that teaches the standard toolchain (Linux, Git, Docker, Kubernetes, Jenkins, Terraform, AWS) plus how to use generative AI in daily DevOps work: writing pipelines and IaC with GitHub Copilot and ChatGPT/Claude, deploying LLM-based applications, and applying AI to monitoring and incident response (AIOps). You finish with both a classic DevOps portfolio and AI-era skills employers now list in job descriptions.
DevOps job descriptions increasingly list GenAI exposure — AI-assisted pipeline authoring, LLM app deployment and AIOps — and candidates who combine DevOps with GenAI skills typically command higher offers than tool-only candidates. Most traditional institutes still teach a 2018-era stack (Puppet, Nagios, Chef); the GenAI layer is what differentiates your profile.
No. This is not a data-science course. You learn to *use* GenAI tools as a DevOps engineer — prompting Copilot/ChatGPT to write Terraform, Kubernetes manifests and pipelines, and deploying/operating LLM applications. No ML math or model training is required.
AI-assisted scripting and IaC (GitHub Copilot, ChatGPT, Claude), prompt patterns for infrastructure code, deploying LLM applications on Kubernetes and AWS, GPU basics and cost control, vector databases at an operations level, and AI-driven monitoring/incident summarization (AIOps).
The core program runs 8 weeks with live weekend classes and a maximum of 10 students per batch. Fees are transparent: L1 Foundation ₹15,000, L2 Complete ₹35,000 (most popular, EMI available), L3 Expert ₹70,000 with full mentorship. See the fees page for a full breakdown of what each level includes.
Live and instructor-led, taught personally by an AWS-certified ex-TCS DevOps Lead. All sessions are recorded and you keep lifetime access to the recordings, with doubt-clearing support during the week.
You get dedicated placement support — resume building, mock interviews and referrals. 500+ students trained with up to 85% placement rate. The GenAI portfolio projects give you interview talking points most candidates simply do not have. A 7-day full-refund policy applies if you are not satisfied.
In your free demo you will build a live CI/CD pipeline from scratch — not watch a slideshow. It takes 45 minutes and shows you exactly what the full course feels like.
Only 3 Seats Left for Next Batch
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