Programme objective: You document and build a full-stack application: requirements, architecture, a database, a frontend, a backend, application programming interfaces, tests, a review, and a pipeline. A large language model assistant may draft. You keep a change only after you can explain it, test it, and review it.
Learner path: Requirement engineering, system architecture, database engineering, programming, design patterns, API development, frontend, backend, Git collaboration, AI code review, software quality, then CI/CD and deployment. The shared gates sit on Complete AI-Native SDLC Engineering.
Suggested time inside the pathway is 40 hours, within a 16-week professional programme of about 160 to 200 hours. That shape is subject to batch and faculty planning. The cohort and any certificate are confirmed when you enquire. This page does not state fees, batch dates, faculty names, or placement rates.
Module 1 — Requirement engineering
You write a business requirements document and a functional requirements document for the application you will build, and you mark every decision those documents leave open.
Module 2 — System architecture
You produce a high-level design, a low-level design, and a unified modeling language sketch, and you can walk a classmate through each box and each boundary.
Module 3 — Database engineering
You model the data, choose keys, and apply normalization, indexing, transactions, and migrations so a second person can see why each table exists.
Module 4 — Programming and AI coding
You implement the behaviour in the language the task names, compare an assistant draft with your design, and rewrite any line you cannot explain.
Module 5 — Software design patterns
You ask an assistant for one pattern from the groups named on this page, check it against the requirement, find an anti-pattern, refactor, and write a unit test. The page names the patterns. It does not implement them.
Module 6 — API development
You design the application programming interface the screens will call, document it with Swagger / OpenAPI, and show a request and a response you can account for.
Module 7 — Frontend development
You build the screens with HTML and CSS in Angular or React, including an empty state and a path a person can finish without a hint from you.
Module 8 — Backend engineering
You build the server with FastAPI, Flask, Django, Node.js, Spring Boot, or ASP.NET Core, and you keep secrets out of the repository.
Module 9 — Git and code collaboration
You branch, commit, and open a review on Git with GitHub or GitLab so a classmate can see what changed and why.
Module 10 — AI code review
You apply the code-review checklist on this page to a change, including a change an assistant wrote, and you record what you kept and what you rejected.
Module 11 — Software quality
You add tests that match the requirement, run them yourself, and write down what passed and what you still have to judge.
Module 12 — CI/CD and deployment
You put the application through a pipeline with Docker and GitHub Actions or GitLab CI. A green run is evidence. A person still approves a release. An assistant recommendation does not deploy the application by itself.
Testing the business result is AI-Powered Test Automation Engineering. Performance and a controlled failure are AI-Driven Performance and Chaos Engineering. Authorised defensive testing is AI-Powered Application, API and LLM Security Testing.
