Build. Deploy. Automate. Operate AI.

Master MLOps
Where AI Meets DevOps

Turn AI/ML models into production-ready systems with DevOps, Cloud, Kubernetes, CI/CD and automation. Master Linux, Cloud, AWS, DevOps, Kubernetes, Infrastructure Automation, Generative AI, Agentic AI and MLOps through one structured, hands-on learning journey.

ENROL NOWTALK TO A COUNSELLOR

Duration

280 Hours

Phases

19 Learning Phases

Practical

100% Practical

Projects

10+3 DevOps and AI Projects

4.8
★★★★★
Google Reviews
4.9
Trustpilot

Reserve Your Seat

Next cohort starts 16 September 2026 — spots fill fast.

Training That Delivers Measurable Results

DevOps AI course - From Linux fundamentals to production-ready LLMOps, every number reflects real career outcomes—not marketing hype.

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Engineers already trained across Linux, Red Hat & DevOps

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Structured, sequential, zero-fluff curriculum 180+ hours Devops and 100 hours of AI/ML and LLMOps

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practical, hands-on, project-based learning with real-world scenarios

0.0x

Compensation premium reported for LLMOps-skilled engineers

0 Major

AI Capstone Projects spanning Generative AI, Agentic AI, RAG and LLMOps

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Highly structured, hands-on modules covering basic Python to advanced Cloud AI

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DevOps and AI/ML projects spanning Linux, Cloud, Kubernetes, CI/CD

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Tooling categories spanning LLM Providers, Vector DBs, and Agentic Frameworks

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Hiring parteners across world of Grras Solutions

The Future of Infrastructure Engineering

WHY DEVOPS ENGINEERS NEED AI SKILLS
AI Is Changing How Modern Engineering Teams Work

AI is no longer limited to data scientists and AI researchers. Modern engineering teams need professionals who understand both:

AI APPLICATIONS + THE INFRASTRUCTURE THAT RUNS THEM

From containers and Kubernetes to LLMs, RAG, AI agents and MLOps, modern AI systems need to be deployed, automated, monitored and operated like production systems.

stats ai mlops
CapabilityTraditional DevOps
Modern MLOps / LLMOpsPowered by Reasoning AI Agents
Primary FocusDeploy applicationsDeploy & manage AI systems
AutomationScripts & CI/CD pipelinesAI agents that reason & automate
InfrastructureContainers & KubernetesGPU clusters + Vector DBs + Kubernetes
MonitoringCPU, Memory, LogsModel quality, latency, hallucinations & cost
DeploymentDocker & HelmLLMs, RAG, AI Agents & Model Serving
DataConfiguration filesEmbeddings, datasets & vector search
Decision MakingManual troubleshootingSelf-healing AI workflows
Career OutlookStable but maturingFastest-growing AI infrastructure roles
ENROL NOWTalk to a Counselor

MLOps Job Roles in India

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)

LLMOps Engineer

₹28L - ₹50L / yr

AI Platform Engineer

₹25L - ₹45L / yr

Agentic Systems Architect

₹35L - ₹60L / yr

Machine Learning Operations (MLOps)

₹20L - ₹40L / yr

AI Infrastructure Specialist

₹22L - ₹38L / yr

Generative AI Solutions Architect

₹30L - ₹55L / yr

NLP/LLM Deployment Engineer

₹24L - ₹42L / yr

Site Reliability Engineer (AI)

₹20L - ₹38L / yr

Data & AI Engineer

₹18L - ₹32L / yr

Computer Vision MLOps

₹22L - ₹40L / yr

LLMOps Engineer

₹28L - ₹50L / yr

AI Platform Engineer

₹25L - ₹45L / yr

Agentic Systems Architect

₹35L - ₹60L / yr

Machine Learning Operations (MLOps)

₹20L - ₹40L / yr

AI Infrastructure Specialist

₹22L - ₹38L / yr

Generative AI Solutions Architect

₹30L - ₹55L / yr

NLP/LLM Deployment Engineer

₹24L - ₹42L / yr

Site Reliability Engineer (AI)

₹20L - ₹38L / yr

Data & AI Engineer

₹18L - ₹32L / yr

Computer Vision MLOps

₹22L - ₹40L / yr
DevOps and AI Engineering

One Programme. Two Disciplines. One Career Path.

Learn DevOps + Cloud Engineering First. Then Apply It to AI.

This programme combines:

180 HOURS

DevOps & Cloud Engineering

Learn the infrastructure foundation required to build and operate modern applications.

Linux →Networking →Ansible →Git →AWS →Terraform →Docker →Kubernetes →CI/CD →Monitoring
PLUS
100 HOURS

Generative & Agentic AI

Build modern AI applications and learn how to take them towards production.

Python →Machine Learning →Deep Learning →LLMs →Prompt Engineering →RAG →AI Agents →Open-Source LLMs →MLOps

TOTAL 280 HOURS OF DEVOPS + AI ENGINEERING

Instead of learning DevOps and AI as disconnected courses, follow one sequential learning journey where infrastructure skills become the foundation for your AI engineering work.

One Programme. Two Disciplines.

DevOps + AI Engineering Curriculum

Phase 01 | DevOps

DevOps Foundations & Networking

In Class | Core Topics
  • DevOps principles, culture, and lifecycle
  • Networking fundamentals for cloud environments
  • Foundation of modern infrastructure and architecture
Phase 02 | DevOps

Linux Administration

In Class | Core Topics
  • Linux system architecture and command-line utilities
  • User administration, permissions, and security
  • Red Hat-aligned operational skills and package management

Build strong Linux administration skills at a Red Hat-aligned level.

Phase 03 | DevOps

Server Management

In Class | Core Topics
  • Essential server configuration and deployment
  • Operational best practices and maintenance
  • System troubleshooting and log management

Learn essential server management and operational practices.

Phase 04 | DevOps

Automation with Ansible

In Class | Core Topics
  • Introduction to Ansible architecture and inventory
  • Writing Playbooks for configuration management
  • Automating repetitive infrastructure tasks

Automate infrastructure and configuration management using Ansible.

Phase 05 | DevOps

Git & GitHub

In Class | Core Topics
  • Version control fundamentals and Git CLI
  • Branching, merging, and conflict resolution strategies
  • Collaborative development and pull requests with GitHub

Learn modern version control and collaborative development workflows.

Phase 06 | Cloud

Virtualisation & Cloud Computing

In Class | Core Topics
  • Hypervisors, VMs, and virtualisation concepts
  • Cloud computing models (IaaS, PaaS, SaaS)
  • Transitioning from on-premise to cloud infrastructure

Understand virtualisation and the fundamentals of cloud computing.

Phase 07 | Cloud

AWS Cloud

In Class | Core Topics
  • AWS Global Infrastructure and IAM
  • Compute (EC2) and Storage (S3, EBS) services
  • Networking (VPC) and database fundamentals

Work with core AWS infrastructure and cloud services.

Phase 08 | Cloud

Terraform

In Class | Core Topics
  • Infrastructure as Code (IaC) principles
  • Writing, planning, and applying Terraform configurations
  • Managing state and automating AWS resource provisioning

Learn Infrastructure as Code and automate cloud infrastructure.

Phase 09 | DevOps

Docker & Containerisation

In Class | Core Topics
  • Containerisation vs Virtualisation
  • Docker architecture, CLI, and Dockerfiles
  • Building, sharing, and running container images

Build and manage containerised applications.

Phase 10 | DevOps

Kubernetes

In Class | Core Topics
  • Kubernetes cluster architecture and core components
  • Deploying Pods, Services, and Deployments
  • Scaling, managing, and operating containerised workloads

Deploy, manage and operate containerised workloads with Kubernetes.

Phase 11 | DevOps

CI/CD

In Class | Core Topics
  • Continuous Integration and Delivery concepts
  • Building automated pipelines with Jenkins
  • Automating CI/CD workflows using GitHub Actions

Build automated delivery pipelines using Jenkins and GitHub Actions.

Phase 12 | DevOps

Monitoring

In Class | Core Topics
  • Infrastructure and application monitoring strategies
  • Metrics collection and alerting with Prometheus
  • Creating dynamic data dashboards with Grafana

Monitor infrastructure and applications using Prometheus and Grafana.

Phase 13 | AI Eng.

Data Science & Machine Learning Foundations

In Class | Core Topics
  • Python for AI and data handling
  • Exploratory Data Analysis (EDA)
  • Core machine learning algorithms and evaluation

Build your understanding of data science and machine learning fundamentals.

Phase 14 | AI Eng.

Deep Learning & LLM Foundations

In Class | Core Topics
  • Neural networks and deep learning concepts
  • Transformers and attention mechanisms
  • Tokenization and foundations of LLMs

Understand deep learning concepts and the foundations behind Large Language Models.

Phase 15 | AI Eng.

Prompt Engineering & GenAI Applications

In Class | Core Topics
  • Advanced prompt engineering techniques
  • Integrating OpenAI, Anthropic, or Google APIs
  • Building functional Generative AI applications

Learn how to design effective prompts and build Generative AI applications.

Phase 16 | AI Eng.

Embeddings, Vector Databases & RAG

In Class | Core Topics
  • Embeddings and semantic similarity search
  • Working with Vector Databases (ChromaDB, Pinecone, etc.)
  • Building and tuning Retrieval-Augmented Generation (RAG) pipelines

Build knowledge-based AI applications using embeddings, vector databases and Retrieval-Augmented Generation.

Phase 17 | AI Eng.

Agentic AI & Frameworks

In Class | Core Topics
  • AI Agent foundations, loops, and tool-calling
  • Single-agent workflows with LangChain and LangGraph
  • Multi-agent orchestration using CrewAI and AutoGen

Build AI agents and multi-agent systems using modern agent frameworks.

Phase 18 | AI Eng.

Open-Source LLMs & Model Optimisation

In Class | Core Topics
  • Local inference servers and Ollama configuration
  • Deploying models like Llama, Mistral, and DeepSeek
  • Model optimization, benchmarking, and quantization basics

Explore open-source models and techniques for optimising AI workloads.

Phase 19 | AI Eng.

MLOps, LLMOps & Cloud AI

In Class | Core Topics
  • MLOps fundamentals, experiment tracking, and versioning
  • Deploying AI applications via Docker, K8s, and CI/CD
  • Managing workloads across AWS Bedrock and Cloud AI services

Learn how AI and ML systems are deployed, managed, monitored and operated in production environments.

Your Modern DevOps + AI Toolkit

Learn the TechnologiesUsed Across the Programme

Technical buyers trust specificity. Master production-grade tools, frameworks, and APIs required to build, deploy, and scale enterprise infrastructure and AI systems.

Core DevOps & Automation

LinuxBashAnsibleJinja2GitGitHub

Cloud & Infrastructure as Code

AWSTerraform

Containers & Orchestration

DockerDocker ComposeKubernetesOpenShift

CI/CD & Observability

JenkinsGitHub ActionsPrometheusGrafana

Machine Learning Base

PythonFastAPINumPyPandasScikit-learnPyTorchTensorFlow

Generative AI & SDKs

OpenAI SDKsAnthropic SDKsGoogle SDKs

Agentic AI Frameworks

LangChainLangGraphCrewAIAutoGen

AI Infrastructure & Vector DBs

ChromaDBFAISSPineconeOllamaHugging Face

MLOps & Cloud AI

MLflowDVCAWS BedrockAWS SageMakerAzure AI StudioGoogle Vertex AI
See How These Tools Map to Real Job Descriptions→
Learn By Building

Don't Just Watch.
Build Real Projects.

The programme includes 10 DevOps projects plus 3 enterprise-focused AI capstone tracks. Your projects are designed to help you apply the concepts you learn across infrastructure, cloud, automation, containers, AI and MLOps.

01. Capstone
incident_agent.py

> Alert Detected: Cluster CPU Spiking

> Analyzing logs via Agentic Workflow...

> Executing Tool: Automated Remediation

Autonomous DevOps Incident Response Agent

Build an AI-powered system designed to assist with incident detection, analysis and response workflows.

AI AgentsTool CallingDevOps WorkflowsIncident Analysis
10

Hands-on DevOps Projects focusing on Cloud, CI/CD, and Containers

02. Capstone

Enterprise Private GPT Knowledge Assistant

Build a private enterprise knowledge assistant using modern LLM, embeddings and RAG technologies.

LLMsEmbeddingsVector DatabasesRAGEnterprise Knowledge
03. Capstone

Multi-Agent Infrastructure Automation System

Build a system where multiple AI agents collaborate on infrastructure automation workflows.

Agent Frameworks
Multi-Agent Systems
DevOps Workflows
Infrastructure Automation
3 Tracks

Enterprise-Focused AI Engineering Capstone Projects

Start Building These Projects
Who Is This Programme For?

Built for Your Next Career Move

Ideal Profile

01

Beginners & Career Switchers

Start from the fundamentals and progressively build DevOps, Cloud and AI engineering skills. No prior Linux, Cloud, Python or ML experience required.

Match Profile

Ideal Profile

02

System Administrators & IT Support

Move from traditional infrastructure into modern cloud, automation and DevOps workflows.

Match Profile

Ideal Profile

03

DevOps & Cloud Engineers

Add Generative AI, Agentic AI, MLOps and LLMOps skills to your existing engineering experience.

Match Profile

Ideal Profile

04

Developers & Software Engineers

Learn how applications are deployed, automated and operated — while adding modern AI development skills.

Match Profile
Start Your Journey
Career Paths

Build Skills for Mordern Engineering Roles

  • •MLOps Engineer
  • •AI / LLMOps Engineer
  • •Agentic AI Engineer
  • •GenAI Application Developer
  • •DevOps Engineer with AI Specialisation
  • •Cloud AI Engineer
  • •Site Reliability Engineer
  • •Platform Engineer
  • •Cloud / Automation Engineer
Talk to a Career Counselor
84%Surge in LLMOps
Demand in 2026
3.2x
Compensation
Premium
Agentic Skills
GRRAS
Certified GenAI &MLOps Engineer
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Companies That Hire From GRRAS

Where skills meet career opportunities.

Companies visit GRRAS to connect with skilled professionals prepared for opportunities in Linux, Cloud, DevOps and modern IT technologies.Through practical training and hands-on learning, GRRAS helps learners develop job-ready technical skills that employers look for.

Technologies and engineering ecosystems shaping modern infrastructure

Build skills relevant to modern DevOps, Cloud and AI-powered engineering workflows.

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Don't replace your DevOps skills. Upgrade them with AI and prepare to work with intelligent infrastructure, AI-powered automation and modern cloud environments.

Why Choose This Programme?

More Than Two Courses Combined

Learn with GRRAS Solutions. Build your skills through a structured programme designed around modern engineering requirements across Linux, Cloud Infrastructure, DevOps, and Applied AI.

One Structured Programme

DevOps and AI are taught as a connected learning journey.

Zero to Production

Start with fundamentals and progress towards production-oriented AI engineering.

Agentic AI Focus

Learn modern AI agent frameworks and build practical agentic systems.

100% Practical

Learn through hands-on labs, projects and implementation.

Full-Stack Tooling

Work across infrastructure, cloud, automation, containers, AI applications and MLOps.

DevOps for AI

Apply DevOps principles to AI and ML workloads.

Real Projects

Build projects that demonstrate your technical capabilities.

Flexible Learning

Choose a weekday intensive or weekend executive format.

Red Hat-Aligned Depth

Build strong Linux and infrastructure foundations aligned with industry-relevant practices.

GRRAS Experience

Learn from an IT training organisation focused on Linux, Cloud, DevOps and Applied AI.

Join 25,000+ Engineers & Students Trained
Flexible Options

Choose Your Learning Format

Whether you want to fast-track your transition or balance learning with your current job, we have a schedule that fits.

Weekday Intensive

Monday – Thursday classes

10Hours/Week

What to expect:

  • Approximately 28 Weeks total duration
  • Structured, intensive learning pace
  • Ideal for accelerating career transition
  • Daily immersion in DevOps & AI
Choose Weekday Intensive
Most Popular

Weekend Executive

Saturday – Sunday classes

05Hours/Week

Includes everything, scheduled for you:

  • Approximately 56 Weeks total duration
  • Flexible weekend-only schedule
  • Designed for working professionals
  • Ample time for self-paced practice
Choose Weekend Executive
Learning Experience

How You Will Learn

Feature

01

Live Interactive Classes

Learn directly with trainers through live sessions.

Explore

Feature

02

Hands-On Cloud Labs

Practise concepts through practical lab environments.

Explore

Feature

03

Recorded Sessions

Revisit lessons when you need additional practice.

Explore

Feature

04

Live Q&A

Get your questions addressed during live interactions.

Explore

Feature

05

Project-Based Learning

Apply your knowledge through DevOps and AI projects.

Explore
Frequently Asked Questions

Got Questions?

Do I need prior Linux experience?
No. The programme starts with Linux and DevOps foundations and progressively moves towards advanced infrastructure and AI topics.
Do I need to know Python?
Do I need Machine Learning experience?
Is the programme practical?
Will I learn Kubernetes?
Will I learn Generative AI?
Will I learn Agentic AI?
Will I learn MLOps and LLMOps?
Is this suitable for working professionals?
What projects will I build?

Ready to Build Your Future in DevOps + AI?

Stop Learning Infrastructure and AI as Separate Skills.
Learn how to BUILD, DEPLOY, AUTOMATE and OPERATE modern AI systems.

DevOps + Cloud + Generative AI + Agentic AI + MLOps

280 Hours19 PhasesHands-On Labs10+ DevOps Projects3 AI Capstones
Enrol Now Talk to a Career Counsellor
Backed by GRRAS Solutions' 25,000+ student legacy+91 6350618066enquiry@grras.com

Future Ops — DevOps + AI Book Free Counseling.