Professional Program in Full Stack Product Engineering With AI

Why Choose Garranto Academy for Full Stack Product Engineering With AI Training?

Garranto Academy delivers a comprehensive, job-ready programme that fuses full-stack engineering mastery with practical AI integration. Over 12 intensive weeks, participants build responsive front-ends, scalable back-ends, and enterprise-grade systems while leveraging AI-assisted development tools to code faster and smarter. The curriculum's hands-on capstone project simulates real-world product engineering—equipping graduates to design, build, and deploy AI-enabled applications that meet modern industry demands.

Course Overview:

The Professional Program in Full Stack Product Engineering With AI is a 12-week, 480-hour immersive training that transforms learners into versatile, AI-augmented software engineers. Through eight meticulously structured modules, participants gain hands-on experience in modern front-end frameworks, Node.js and Spring Boot back-end development, DevOps and cloud deployment, application security, and AI API integration. The programme culminates in a 3-week guided capstone where learners plan, implement, and deploy a complete AI-powered full-stack application—building a production-ready portfolio piece that demonstrates end-to-end product engineering proficiency.

What You'll Learn in Our Professional Program in Full Stack Product Engineering With AI Course?

Course Objectives:

Upon successful completion of this course, learners will be able to:

• ✓Apply core software engineering principles and follow professional coding standards to produce clean, reliable code.

• ✓Build responsive and scalable front-end applications using modern web technologies and UI frameworks.

• ✓Design, develop, and implement secure back-end services and REST APIs using Node.js and related technologies.

• ✓Develop enterprise-grade backend applications using the Spring Framework and apply relevant architectural patterns.

• ✓Identify, prevent, and mitigate common security vulnerabilities by applying secure coding practices aligned with OWASP guidelines.

• ✓Implement DevOps workflows by building CI/CD pipelines, containerizing applications, and deploying to cloud environments.

• ✓Integrate AI capabilities into software systems using APIs, automation techniques, prompting approaches, and RAG concepts.

• ✓Plan, build, and deliver a complete AI-enabled full-stack application through a guided capstone project that simulates real-world product engineering.

Prerequisites

• ✓Basic digital literacy and computer usage proficiency.

• ✓Ability to read, write, and communicate effectively in English.

• ✓Prior programming experience is preferred but not mandatory.

Course Outlines:

Module 1 — Software Engineering Foundations & AI-Augmented Development

• ✓Key Concepts:

• ✓Core software engineering concepts: algorithms, data structures, complexity basics

• ✓Version control (Git/GitHub), branching strategies, and repository hygiene

• ✓Debugging strategies, error handling patterns, and structured problem-solving

• ✓Code quality frameworks: SOLID principles, DRY, KISS, modularization

• ✓Professional developer habits: code reviews, pair programming, documentation standards

• ✓AI-assisted development workflows using GitHub Copilot, Cursor, v0.dev

• ✓Using AI to generate unit tests, refactor code, and improve technical documentation

• ✓Automation of repetitive coding tasks using AI prompts

Module 2 — Modern Front-End Development & Interactive UI Engineering

• ✓Key Concepts:

• ✓Deep dive into HTML5 semantics, CSS architecture (BEM, utility-first patterns), and modern JS ES6+ features

• ✓Responsive layouts using Flexbox, Grid, and mobile-first design principles

• ✓Component-driven development using frameworks (React/Angular)

• ✓State management (Redux, Zustand, Context API, or Angular Services)

• ✓Routing, lifecycle management, and rendering optimization

• ✓Building reusable design systems and UI components

• ✓AI-assisted workflows: generating UI mockups, converting wireframes to code, optimizing CSS and components

• ✓Performance tuning: code splitting, lazy loading, image optimization

Module 3 — Back-End Development, APIs & Application Architecture with Node.js

• ✓Key Concepts:

• ✓Server-side architecture using Node.js and Express

• ✓Designing RESTful APIs with proper routing, controllers, services, and middleware

• ✓Data modelling and persistence with SQL (PostgreSQL/MySQL) and NoSQL (MongoDB/DynamoDB)

• ✓Environment configuration, error handling, logging, and security middleware

• ✓Caching strategies (Redis), rate-limiting, and performance tuning

• ✓API documentation using OpenAPI/Swagger

• ✓Testing approaches: unit tests (Jest), integration tests, mocking services

• ✓AI-assisted backend development for debugging, test generation, and architecture review

Module 4 — Enterprise Back-End Development with Spring Framework

• ✓Key Concepts:

• ✓Spring Boot architecture: controllers, services, repositories, configuration

• ✓Inversion of Control (IoC) and Dependency Injection in Spring

• ✓Creating REST APIs with Spring Web and Spring Data

• ✓Database integration: JPA/Hibernate, entity mapping, query optimization

• ✓Microservices fundamentals: API gateways, service discovery, configuration servers

• ✓Messaging using Kafka/RabbitMQ (introductions and use cases)

• ✓Introduction to Spring Security for authentication and authorization

• ✓Best practices for building robust enterprise-grade features

• ✓Integration testing and environment configuration in Spring

Module 5 — Secure Coding, Application Security & Cybersecurity Essentials

• ✓Key Concepts:

• ✓OWASP Top 10 deep dive: XSS, SQL injection, CSRF, IDOR, SSRF, etc.

• ✓Secure session management, token handling, and password storage

• ✓Implementing OAuth2.0 & JWT authentication flows

• ✓API security: input validation, schema validation, rate-limits, throttling

• ✓Secrets management using tools like Vault, AWS Secrets Manager, or environment vaulting

• ✓Logging security events and intrusion detection basics

• ✓Static and dynamic analysis tools (SAST/DAST)

• ✓AI-assisted security scanning and vulnerability triaging

Module 6 — DevOps Engineering, Cloud Deployment & Continuous Delivery

• ✓Key Concepts:

• ✓Principles of CI/CD and GitOps workflows

• ✓Creating pipelines using GitHub Actions, GitLab CI, or Jenkins

• ✓Docker fundamentals: images, containers, volumes, Dockerfiles

• ✓Building deployable artifacts and containerizing full-stack apps

• ✓Cloud deployment options: AWS, GCP, Azure

• ✓Infrastructure as Code (IaC) basics using Terraform or CloudFormation

• ✓Monitoring and observability: Logs (ELK/OpenSearch), Metrics (Prometheus/Grafana), Tracing fundamentals

• ✓AI-assisted DevOps automation: triage workflows, deployment suggestions, environment templates

Module 7 — AI Integration for Software Systems — APIs, Automation & Intelligent Features

• ✓Key Concepts:

• ✓Integrating AI using REST APIs (OpenAI, Anthropic, Gemini, custom endpoints)

• ✓Prompt engineering for developers: system prompts, structured prompts, chain-of-thought scaffolding

• ✓Lightweight model customization: prompt templates, metadata-driven responses

• ✓RAG architecture fundamentals: vector stores, embeddings, retrieval pipelines

• ✓Building real AI features: chat interfaces, document intelligence workflows, email/CRM automation, AI code assistants

• ✓Real-time AI integration (WebSockets, streaming responses)

• ✓Handling AI failure modes, hallucinations, and fallback mechanisms

Module 8 — Capstone Project: Full-Stack Product Engineering & AI Integration

• ✓Key Concepts:

• ✓Product scoping: user journeys, requirements, acceptance criteria

• ✓Designing end-to-end architecture (front-end, backend, database, AI service, DevOps pipeline)

• ✓Sprint planning: backlog creation, milestone setting, iteration cycles

• ✓Full-stack implementation using technologies from Modules 1–7

• ✓Integrating at least one AI-powered feature (RAG, automation, chat, etc.)

• ✓Deploying to cloud with CI/CD in place

• ✓Load testing, error monitoring, and performance validation

• ✓Final MVP demonstration to instructors and evaluators

• ✓Post-deployment reflection: lessons learned, improvements, documentation

Course Outcomes:

Upon completing the "Professional Program in Full Stack Product Engineering With AI" course, participants will be able to:

• ✓Demonstrate practical software engineering skills by writing clean, maintainable, and well-structured code following industry standards.

• ✓Build responsive and user-friendly front-end interfaces using modern UI frameworks and component-based architectures.

• ✓Develop secure, scalable back-end services and REST APIs using Node.js and associated technologies.

• ✓Create enterprise-grade backend applications using the Spring Framework and appropriate architectural patterns.

• ✓Apply secure coding practices and identify vulnerabilities in accordance with OWASP guidelines, implementing effective protection measures.

• ✓Configure and operate CI/CD pipelines, apply DevOps principles, and deploy applications to cloud environments.

• ✓Integrate AI capabilities into applications using APIs, automation workflows, and RAG-based enhancements.

• ✓Plan, build, and deliver a full-stack, AI-enabled web product that is deployable and demonstrates end-to-end product engineering proficiency.

Key Benefits of Learning Professional Program in Full Stack Product Engineering With AI

Become a versatile, AI-augmented full-stack engineer ready for modern product teams. This programme gives you mastery across front-end, back-end, enterprise frameworks, cloud, and AI integration—all reinforced by a real-world capstone. You'll learn to code faster with AI copilots, secure your applications against OWASP threats, and ship production-grade software, giving you a powerful portfolio and a direct pathway to high-demand software engineering and AI-enabled development roles.

How Full Stack Product Engineering with AI Transforms Software Development

AI-augmented full stack engineering collapses the gap between idea and deployment. Developers who can harness AI throughout the stack—from generating UI code to building intelligent APIs—ship features faster, reduce defects, and create adaptive applications that evolve with user needs. This programme instils that transformative mindset, enabling you to build scalable, secure, and intelligent products that drive digital innovation across fintech, e-commerce, SaaS, and beyond.