M1: Software Engineering Foundations & AI-Augmented Development
To equip learners with foundational software engineering principles, strong problem-solving techniques, professional coding habits, and the ability to use AI-powered developer tools (Copilot, v0.dev, Cursor, etc.) to enhance productivity, documentation, and code quality.
Software engineering principlesProblem-solving techniquesProfessional coding habitsUsing AI-powered developer tools (Copilot, v0.dev, Cursor)Improving productivity, documentation and code quality
M2: Modern Front-End Development & Interactive UI Engineering
To enable learners to build responsive, interactive, and modular user interfaces using HTML, CSS, JavaScript, and React/Angular, while leveraging AI-assisted workflows for faster prototyping, UI generation, and code refinement.
Responsive and interactive UI developmentHTML, CSS and JavaScriptReact and/or Angular fundamentalsModular front-end architectureAI-assisted prototyping, UI generation and refinement
M3: Back-End Development, APIs & Application Architecture with Node.js
To train learners to design, build, and maintain scalable backend systems using Node.js and Express, implement RESTful APIs, work with databases, apply architecture patterns, and use AI tools to improve testing, debugging, and documentation quality.
Backend development with Node.js and ExpressDesigning and implementing RESTful APIsWorking with relational & NoSQL databasesApplying backend architecture patternsUsing AI tools for testing, debugging and documentation
M4: Enterprise Back-End Development with Spring Framework
To give learners hands-on experience building enterprise-grade backend applications using the Spring Framework, with emphasis on architectural patterns, dependency injection, microservices foundations, enterprise security practices, and building production-ready features typical in large-scale corporate systems.
Spring Framework and Spring BootDependency injectionMicroservices foundationsEnterprise security practicesBuilding production-ready enterprise features
M5: Secure Coding, Application Security & Cybersecurity Essentials
To equip learners with practical secure coding skills, knowledge of OWASP Top 10 risks, secure API design, OAuth-based authentication and authorization, secrets management, and AI-supported vulnerability detection.
Secure coding practicesUnderstanding OWASP Top 10 risksSecure API designOAuth-based authentication and authorizationSecrets managementAI-supported vulnerability detection
M6: DevOps Engineering, Cloud Deployment & Continuous Delivery
To train learners to build CI/CD pipelines, deploy full-stack and enterprise applications to the cloud, use Docker, implement monitoring, and apply AI-assisted automation.
CI/CD pipeline design and implementationDeploying applications to the cloudContainerisation with DockerMonitoring and basic observabilityAI-assisted automation in DevOps workflows
M7: AI Integration for Software Systems: APIs, Automation & Intelligent Features
To enable learners to integrate AI capabilities into modern software systems by understanding how AI models are accessed via REST APIs, how prompts function as lightweight training, how RAG enhances model accuracy, and how AI features can be embedded into applications such as chat interfaces, document workflows, IDE tools, and real-time product experiences.
Integrating AI models via REST APIsPrompt design as lightweight trainingUnderstanding and applying RAGEmbedding AI in chat interfaces and document workflowsBuilding AI-enabled IDE tools and real-time product experiences
M8: Capstone Project: Full-Stack Product Engineering & AI Integration
To guide learners through the full product engineering lifecycle—from ideation, requirement gathering, and product thinking to architecture, development, iteration, and deployment—using agile workflows and integrating AI-driven features. Learners will work like real product teams: defining user problems, planning sprints, building MVPs, integrating AI capabilities, deploying to the cloud, and presenting a production-ready solution.
End-to-end product engineering lifecycleIdeation, requirements and product thinkingDesigning architecture for full-stack solutionsAgile workflows and sprint-based executionIntegrating AI capabilities into productsCloud deployment of the capstone solutionPresenting a production-ready product
