AI for Developers: Coding with AI, RAG, Agentic Workflows, and Security
This course provides technology professionals with a practical framework for integrating artificial intelligence into the modern software development lifecycle and data engineering processes.
Advanced20 Hours
Live-Zoom
Certified
About the Course
AI For Modern SDLC And Data Engineering
Advanced Level20 HoursAI In SDLCMLOps
An advanced, 20-hour course that equips technology professionals with a practical framework to integrate AI into the software development lifecycle and data engineering, enabling context-aware applications, autonomous workflows, higher productivity, and maintained code quality and system reliability.
Category
Software Developers, Data And Machine Learning Engineers, Technical Architects, Technology Leaders, IT Professionals
Curriculum
Hands-On, Tool-Based Learning
Learning Outcomes
- Design RAG pipeline architectures that integrate enterprise knowledge with LLMs
- Increase coding productivity with AI-powered development tools while maintaining code quality standards
- Build agentic workflows capable of autonomously solving complex, multi-step tasks
- Systematically manage vulnerabilities and technical debt using AI-driven analysis
- Structure MLOps processes to manage the full AI model lifecycle end-to-end
Who Should Attend?
- Software Developers
- Data And Machine Learning Engineers
- Technical Architects
- Technology Leaders
- IT Professionals
Prerequisites
- Basic interest in the field
- Computer and internet access
Advanced, 20 Hours
Hands-on, tool-based learning to integrate AI into the software lifecycle and data engineering.
Course Content
AI Engineering Curriculum
LLMs to ProductionDeveloper Workflow AccelerationEnterprise RAG and AgentsSecurity and MLOps Focus
A structured program that builds practical AI engineering skills—from LLM fundamentals and prompt design to RAG architectures, agentic systems, security, and MLOps—tailored for modern software teams.
Tools
- GitHub Copilot
- Cursor
- Windsurf
- LangChain
- Snyk
- Weaviate
1 Module 1 LLM Fundamentals and Their Role in Modern Software Development
- Understand how large language models work, their capabilities, and limitations
- Identify integration points in modern development workflows
- Map high-value use cases for LLMs in software projects
Duration: 2 hours
2 Module 2 Prompt Engineering and Advanced Design Patterns
- Create reusable prompt templates for common tasks
- Apply prompt chaining to structure multi-step reasoning
- Design prompts for more deterministic and controllable outputs
Duration: 2 hours
3 Module 3 AI-Augmented Development Workflows
- Use AI for coding, refactoring, debugging, and test generation
- Adopt tools and best practices that accelerate daily development
- Incorporate AI assistance into existing team workflows
Duration: 2 hours
4 Module 4 RAG (Retrieval-Augmented Generation) Fundamentals and Architecture Design
- Learn embeddings, vector databases, retrieval, and generation flow
- Design end-to-end RAG system architectures
- Integrate enterprise data sources with LLMs
Duration: 3 hours
5 Module 5 Advanced RAG Applications and Optimization
- Apply chunking strategies for better retrieval
- Optimize retrieval and context management
- Improve output quality and reliability
Duration: 3 hours
6 Module 6 Agentic AI Systems and Autonomous Workflows
- Explore agent architectures for tool use and multi-step tasks
- Integrate tools and services into agent workflows
- Orchestrate agents for planning and execution
Duration: 3 hours
7 Module 7 Security, Risk, and Technical Debt Management in AI Systems
- Recognize risks: prompt injection, data leakage, and model abuse
- Use AI-assisted security testing and vulnerability detection
- Manage AI-related technical debt and risk mitigation
Duration: 2 hours
8 Module 8 MLOps and AI System Lifecycle Management
- Handle model deployment, monitoring, and versioning
- Establish continuous improvement loops in production
- Operate AI applications reliably at scale
Duration: 3 hours
