DevOps Pro Syllabus
About The Training
Modern software organizations rely on fast, reliable, and secure software delivery. DevOps Engineers bridge software development, infrastructure, cloud platforms, automation, and operations, enabling teams to build, deploy, and manage applications efficiently throughout the software lifecycle.
The DevOps Pro program at Infinity Labs R&D is designed for candidates with a technical background and practical experience in coding or IT who want to integrate into DevOps and Cloud roles at the core of development teams. Guided by industry Tech Leaders, participants gain hands-on experience with modern DevOps methodologies, cloud infrastructure, automation, container platforms, monitoring, and production environments while working in small teams that simulate real R&D organizations.
As AI continues to transform software engineering, the program also introduces modern AI-powered DevOps workflows, including LLMs, Prompt Engineering, AI Agents, Agentic DevOps, and Model Context Protocol (MCP), preparing participants to leverage AI tools throughout the software delivery lifecycle.
Stage 1: Software Engineering and DevOps Foundations
Key skills and technologies:
- Linux Administration
- Shell and Bash
- Python scripting
- Software Development Lifecycle (SDLC)
- Code management
- Git workflows
- Disciplined Agile Delivery
- Build automation
- Issue tracking
- Integration testing
- Automated testing
- CI/CD principles
- Build artifact repositories
- Virtual Machines (VM)
- Service Virtualization
- Databases fundamentals
- LLM fundamentals
- Prompt Engineering
Stage 2: Cloud Infrastructure, Automation and Container Platforms
This stage focuses on the technologies and practices used to automate software delivery and manage cloud-native infrastructure. Participants learn to build deployment pipelines, automate infrastructure, manage containerized applications, and operate scalable cloud environments using industry-standard DevOps tools.
Key skills and technologies:
- CI/CD in depth (Jenkins)
- Cloud Management (AWS)
- Infrastructure as Code
- Configuration and automation
- Docker
- Container management
- Kubernetes
- Orchestration
- Microservices
- Staging environments
- Production environments
- Release management
- GitOps
- ArgoCD
- Load balancing
- AI-assisted automation
- AI Agents
Stage 3: Production Operations, Observability and AI-Enhanced DevOps
In the final stage, participants integrate development, infrastructure, and operations into complete production workflows. The focus is on operating reliable systems, monitoring application health, improving security, troubleshooting production environments, and applying AI-powered tools to increase operational efficiency and automation.
Core focus areas include:
- Monitoring and Observability
- Incident management process
- DevSecOps principles
- Production operations
- Kafka
- Big Data fundamentals
- Automated operational workflows
- Production troubleshooting
- System reliability
- Agentic DevOps
- Model Context Protocol (MCP)
- AI-powered operational workflows
- Best practices, methodology and workflow