5000+ Professional Trained
The only 100-hour ML & Generative AI program built for Linux, Cloud & DevOps engineers — not data scientists. Zero ML background required.
Next Batch
05 September
Duration
100 Hours
Training Mode
Online
100%
Job Oriented Course
Next cohort starts 05 September 2026 — spots fill fast.
From Linux fundamentals to production-ready LLMOps, every number reflects real career outcomes—not marketing hype.
Engineers already trained across Linux, Red Hat & DevOps
Structured, sequential, zero-fluff curriculum
Of the program is pure Generative AI & Agentic systems
Compensation premium reported for LLMOps-skilled engineers
Total duration of the comprehensive, sequential learning journey
Highly structured, hands-on modules covering basic Python to advanced Cloud AI
Learning phases progressing from foundations to a production capstone project
Tooling categories spanning LLM Providers, Vector DBs, and Agentic Frameworks
Hiring parteners across world of Grras Solutions
Classical automation—Bash scripts, Jenkins pipelines, and Ansible playbooks—is evolving into intelligent infrastructure powered by reasoning AI agents. Modern organizations aren't just hiring DevOps engineers anymore—they're hiring engineers who can deploy, monitor, and scale AI systems in production.
Engineers who understand infrastructure + AI will define the next decade.
| Capability | Traditional DevOps | Modern MLOps / LLMOpsPowered by Reasoning AI Agents |
|---|---|---|
| Primary Focus | Deploy applications | Deploy & manage AI systems |
| Automation | Scripts & CI/CD pipelines | AI agents that reason & automate |
| Infrastructure | Containers & Kubernetes | GPU clusters + Vector DBs + Kubernetes |
| Monitoring | CPU, Memory, Logs | Model quality, latency, hallucinations & cost |
| Deployment | Docker & Helm | LLMs, RAG, AI Agents & Model Serving |
| Data | Configuration files | Embeddings, datasets & vector search |
| Decision Making | Manual troubleshooting | Self-healing AI workflows |
| Career Outlook | Stable but maturing | Fastest-growing AI infrastructure roles |
Our programme prepares infrastructure, DevOps, and backend professionals for high-growth roles in AI operations. From deploying resilient LLM pipelines to managing autonomous agent architectures, graduates are ready for impactful roles across tech, finance, enterprise SaaS, and fast-growing AI startups.
Showing 2026 estimated average salaries (India)
Not Beginners to Tech, Just Beginners to AI. Let the right person self-identify fast.
Linux Engineers & SysAdmins ready to move up the value chain
DevOps Engineers who want AI/ML and agentic systems in their toolkit
Cloud Engineers (AWS / Azure / GCP) specializing in AI infrastructure
RHCE-track professionals ready for their next career milestone
GRRAS Alumni upgrading existing certifications with in-demand AI skills
Working professionals who know scripting but have zero ML background
Every lab, every project, every example is infrastructure-flavored — because that's what you'll actually be paid to build.
Framed entirely around infra, cloud & DevOps use cases
19 of 100 hours dedicated purely to autonomous agents
From Python basics to a deployed, containerized AI app
18 modules, 18 labs, 1 capstone. No theory-only modules.
LangChain, LangGraph, CrewAI, AutoGen, RAG, vector DBs
Real MLOps/LLMOps: CI/CD, containers, observability
10, 8, or 5 hrs/week to fit a working professional's life
5,000+ alumni legacy in Linux, Red Hat & Cloud
Master how neural networks, transformers & LLMs actually work under the hood.
Build a working LLM-powered application from scratch using modern API design.
Construct a full RAG pipeline - building a private enterprise Company GPT system.
Autonomous single- & multi-agent systems using LangChain, LangGraph, CrewAI & AutoGen.
Run, quantize, and benchmark Llama, Mistral & DeepSeek models locally with Ollama.
CI/CD pipelines, containerized deployments, and real-time observability for AI systems.
A fully deployed, portfolio-ready Agentic AI system on cloud infrastructure.
Technical buyers trust specificity. Master production-grade tools, frameworks, and APIs required to build, deploy, and scale enterprise AI systems.
Choose one industry-grade capstone track. Walk away with a working, containerized, deployed application — plus an architecture diagram and a recorded demo.
> Incident Alert Detected: HTTP 500
> Querying Vector Logs via RAG...
> Executing Remediation Runbook...
RAG + agent + tool calling + deployment. Reads alerts, checks logs via RAG, and executes automated runbook remediation.
Production-Ready & Fully Containerized Portfolio Deliverable
Secure, multi-tenant RAG system built with role-based access control (RBAC) and high-performance vector search.
Specialized AI agents (CrewAI / AutoGen) collaborate autonomously to audit cloud security postures and generate production Terraform code.
Deployed App + Architecture Diagram + Recorded Interview Demo
AI isn't replacing DevOps engineers. It's replacing the ones who stopped upgrading. Every CI/CD pipeline, every monitoring stack, every incident response workflow is being rewired around LLMs and autonomous agents — right now, in 2026. This is your structured, sequential path from zero ML knowledge to deploying production-grade Agentic AI systems, taught by the same instructors who've already trained 5,000+ engineers.
Talk to CounsellorEnrollment is open. Cohorts are seat-limited. Your next career milestone starts with one free counseling call.
① Register Online
② Free 1-on-1 Counseling
③ Instant Portal & Lab Access
④ Live Cohort Launch