Build, Deploy & Scale with FutureOps GenAI for DevOps
Transform your continuous delivery workflows with Generative AI. Build self-healing systems, automate Infrastructure-as-Code generation, and resolve production incidents in real-time using agentic AI.
GenAI for DevOps Engineers
Learning Mode
Live + Self-Paced
Instructor-led sessions, hands-on labs, projects, and lifetime access.
Who Should Join
DevOps Engineers & Developers
Perfect for Software Engineers, Cloud Engineers, SREs, Platform Teams and AI enthusiasts.
What You'll Build
AI Agents & Automation
Build autonomous AI agents, RAG pipelines, LLM applications and enterprise workflows.
Technologies
Production AI Stack
OpenAI, Anthropic, LangChain, Vector Databases, LangGraph, CrewAI, Claude, Gemini, Ollama, MLflow.
FutureOps for Infrastructure
Upgrade your existing cloud and system administration toolkit with modern GenAI capabilities. This track emphasizes agentic application development and LLM integration, teaching you to wire AI directly into the platforms and pipelines you already manage.
Agentic Workflows
Master autonomous single- and multi-agent systems using LangChain, LangGraph, CrewAI, and AutoGen. Move beyond simple chatbots to build self-healing loops and automated incident response tools that can execute runbook remediation scripts autonomously.
LLMs & Enterprise RAG
Build a deep understanding of tokens, context windows, and hallucination mitigation. Learn to design multi-step prompts and construct Retrieval-Augmented Generation (RAG) pipelines to build secure, private 'Company GPT' knowledge assistants.
5,000+
Engineers successfully trained across Linux, Red Hat, Cloud & DevOps tracks. Join an established, trusted alumni network.
10+ Years
Of established leadership in IT training, empowering working professionals to step into high-impact infrastructure careers.
100%
Practical, lab-first teaching style. We eliminate abstract data science math in favor of real terminal environments and production tools.
Generative AI for Cloud & DevOps.Production-grade Agentic systems.
Master Python, LLM APIs, Vector Databases, and Multi-Agent frameworks to automate real infrastructure and deploy self-healing CI/CD pipelines.
FoundationsGenAI CoreAgentic AICloud & DeployFoundationsGenAI CoreAgentic AICloud & DeployWho is this GenAI course for?
| Student Profile | Career Objective | Action |
|---|---|---|
| DevOps Engineers | Add Agentic systems and generative workflows to your existing automation toolkit. | |
| Cloud Engineers | Specialize in AWS/Azure/GCP AI infrastructure and deploy secure RAG applications. | |
| Linux & SysAdmins | Move up the value chain by integrating autonomous incident response agents. | |
| General / Non-IT Students | Seeking a generic data science bootcamp without infrastructure context. |
Enabling You to Excel
GenAI Curriculum for You
Foundations of Data & ML
- How to use Python to manipulate, clean, and organize data for analysis?
- How to understand data types, missing values, and visualization?
- How to train, evaluate, and deploy foundational machine learning models?
LLM Fundamentals & Prompting
- How do tokens, context windows, temperature, and sampling affect LLM applications?
- How to design structured prompts, few-shot prompts, and multi-step AI workflows?
- How to build robust GenAI applications using OpenAI, Anthropic, and Google Gemini APIs?
Understand how modern LLMs work and learn to design prompts and applications using leading GenAI APIs.
Vector Databases & RAG
- How to create embeddings and perform semantic similarity search?
- How to build document ingestion, chunking, and retrieval pipelines using ChromaDB, FAISS, or Pinecone?
- How to apply context injection, reranking, and hallucination reduction in RAG systems?
Build production-ready Retrieval-Augmented Generation pipelines for private and enterprise AI knowledge systems.
Agentic AI Systems (Core)
- How to design agent loops, ReAct workflows, planning, and tool calling?
- How to connect AI agents to APIs, infrastructure tools, logs, and external systems?
- How to build agentic workflows using LangChain and LangGraph?
Build autonomous AI agents that reason, use tools, retrieve information, and execute multi-step workflows.
Multi-Agent AI & Open Source LLMs
- How to orchestrate specialized AI agents using CrewAI and AutoGen?
- How to design manager-worker, sequential, hierarchical, and human-in-the-loop agent workflows?
- How to run and benchmark Llama, Mistral, DeepSeek, and other open-source models using Ollama?
Build collaborative multi-agent systems and run powerful open-source LLMs locally using modern AI runtimes.
GenAI Deployment & LLMOps
- How to containerize and deploy RAG and Agentic AI applications using Docker and Kubernetes?
- How to implement CI/CD, model and prompt versioning, monitoring, latency, cost, and quality tracking?
- How to deploy GenAI applications using AWS Bedrock, Azure AI, or Google Vertex AI?
Take GenAI and agentic applications from development to production with containers, CI/CD, monitoring, and cloud AI.
Production GenAI Capstone
- How to build an Autonomous DevOps Incident Response Agent that reads alerts, analyzes logs, and proposes remediation?
- How to build an Enterprise Private GPT using RAG, agents, vector search, and role-based access?
- How to build a Multi-Agent Infrastructure Automation system using CrewAI/AutoGen, cloud tools, and Terraform?
Build and deploy a portfolio-ready production GenAI system that combines RAG, agents, tool calling, and cloud infrastructure.
Agentic AI Skills You'll Gain
Learn everything from Prompt Engineering and LLMs to RAG, AI Agents, LangGraph, CrewAI, Vector Databases, and production-ready autonomous AI systems.
Concepts
Skills
Python & ML Basics
LLM Foundations
Vector DB & RAG
Agentic Frameworks
Multi-Agent Systems
Why GenAI From Grras
What Makes this GenAI Course Best from Others
Built for DevOps Engineers
Not for data scientists — every module is framed around real infrastructure, cloud, and DevOps use cases (log analysis, incident response, runbook automation).
Agentic AI Core Focus
19 of the 100 hours are dedicated purely to autonomous AI agents and multi-agent frameworks, the single most in-demand GenAI skill in tech.
Zero-to-Production Curriculum
Starts with Python fundamentals for absolute beginners, and ends with a deployed, containerized, CI/CD-driven AI application.
100% Hands-On Learning
18 modules, 18 hands-on labs/projects, and 1 capstone. Every concept is immediately applied, never just theory.
Full-Stack GenAI Tooling
LangChain, LangGraph, CrewAI, AutoGen, RAG pipelines, vector DBs, and multi-provider LLM APIs (OpenAI, Anthropic Claude, Google Gemini).
DevOps Discipline Applied to AI
Teaches containerizing, deploying, monitoring, and CI/CD-ing AI systems, not just building them.
Flexible Pacing Options
10 hrs/week (10 wks) | 8 hrs/week (~12 wks) | 5 hrs/week weekend (20 wks) to fit around working professionals' schedules.
Backed by GRRAS Brand Trust
From India's trusted DevOps & Cloud training leader — backed by GRRAS's track record of training 5,000+ engineers in Linux, Red Hat & DevOps.
Generative AI Industry
Career Report '26
Annual AI Talent Demand Growth (India)
Average GenAI Salary
Average CTC
Engineer CTC
Reported CTC
Salary Premium vs IT Services
Our Recruiters
Launch Your Career in Generative AI
Master the complete Generative AI stack—from prompt engineering and LLMs to RAG, AI agents, vector databases, embeddings, and production deployment. Build real-world projects, create an industry-ready portfolio, and prepare for high-demand AI roles with expert mentorship.
Master GenAI
for Infrastructure
Level up your Cloud and DevOps skills by building self-healing Agentic AI pipelines.