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Complete AI-Native SDLC Engineering

RIA – School of Technology & AI · Under AI Essentials

One pathway, four programmes, a person at every gate

Complete AI-Native SDLC Engineering is the shared software development life cycle (SDLC) for the four software programmes on AI Essentials. It is not on NVIDIA AI Essentials. The programme is for people who will engineer, test, secure, optimise, and deploy software with artificial intelligence across the lifecycle, not only generate code.

It is for developers, full-stack engineers, QA and automation testers, performance engineers, application security engineers, DevSecOps engineers, AI application developers, architects, and technical leads. You will meet an application programming interface (API) and, where the work includes a model, a large language model (LLM). Classes run at Purasawalkam and Saligramam. Call +91 98843 63200.

The four programmes

Each page keeps its public title. The enquiry form keeps the existing Technology course name.

The shared lifecycle

You move through these gates in this order. Every gate has evidence, a pass or fail decision, and a human owner. An AI recommendation does not authorise a production deployment by itself.

  1. 1. Requirement engineering. You write a business requirements document (BRD) and a functional requirements document (FRD), and you mark what they do not decide.
  2. 2. Architecture and design. You record a high-level design (HLD), a low-level design (LLD), and a unified modeling language (UML) view a classmate can walk through.
  3. 3. Development and programming. You build the change in the repository and you can explain every line you keep.
  4. 4. Code review and quality gates. A person reviews the change, including a change an assistant wrote. An AI-generated fix is tested and reviewed before it is kept.
  5. 5. Functional and automation testing. The expected business result is written first. A test that passes by changing that result is not a pass.
  6. 6. Performance and reliability. You compare a run with the agreed target, on a system you are allowed to test, and you stop at the ceiling.
  7. 7. Application and AI security. Static application security testing (SAST), dynamic application security testing (DAST), and software composition analysis (SCA) use the current OWASP (Open Worldwide Application Security Project) lists. You report to the owner.
  8. 8. DevSecOps and deployment. Continuous integration and continuous delivery (CI/CD) produces evidence. A signed artifact still waits for a person.
  9. 9. Production operations. You watch the release you were allowed to ship, and you name the owner who can roll it back.

Quality gates you learn to apply

These are release-review teaching points. They are a classroom list. They are not a statement that the RIA website enforces this list on itself.

  • No unresolved critical vulnerabilities.
  • No hardcoded secrets.
  • Mandatory tests pass.
  • Critical dependency findings are triaged.
  • UI and API regression pass.
  • Dynamic findings are triaged by severity.
  • Performance is compared with the agreed target.
  • The release is approved by a person.

An informational finding does not automatically fail a build. Severity, context, and false positives are reviewed.

Foundations the development programme applies

Languages: Python, JavaScript, TypeScript, Java, C# / .NET, SQL, HTML and CSS. Concepts: data structures and algorithms, object-oriented and functional programming, SOLID, DRY, KISS, YAGNI, clean architecture, hexagonal architecture, domain-driven design, MVC, MVVM, layered architecture, microservices, REST, GraphQL, WebSockets, authentication and authorization, role-based access control (RBAC), OAuth 2.0, normalization, indexing, transactions, and migrations.

Design-pattern groups, named and not implemented on this page: creational Singleton, Factory, Abstract Factory, Builder, Prototype; structural Adapter, Decorator, Facade, Proxy, Composite; behavioral Strategy, Observer, Command, State, Template Method; enterprise Repository, Unit of Work, Dependency Injection, CQRS; distributed Circuit Breaker, Retry, Saga, API Gateway, Bulkhead. The exercise — ask an assistant for one pattern, check it, find an anti-pattern, refactor, and write a unit test — is on AI-Native Software Engineering and Development.

Specialist subjects

An AI-generated fix is tested and reviewed before it is kept.

  • Code intelligence. You ask an assistant to explain a change, then you check the explanation against the code.
  • Agentic development with human approval. An agent may propose a change. A person approves it before it is kept.
  • Review agents. A review agent comments. You accept or reject each comment.
  • Test generation. A generated test is kept only when the expected result comes from the requirement.
  • Self-healing automation. A repaired locator is kept only when the business result stays the same.
  • Root-cause analysis. You compare an assistant’s cause with the log and with the change.
  • Performance engineering. You read a run against the target you wrote down first.
  • Security triage. You rate a finding, and a person decides the priority.
  • LLM evaluation. You check a model answer against facts you can point to.
  • Retrieval-augmented generation testing. You check that a retrieval-augmented generation (RAG) answer stays inside the sources the owner authorised.
  • Model context protocol permissions. A tool call stays inside the permission you set, and a person approves an action that changes data.
  • AI governance. You record what the assistant did, who approved it, and what was rejected.

Time and assessment

The planned shape is a 16-week professional programme, 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.

Suggested splitHours
Software engineering and development40
Software testing and automation35
Performance and chaos engineering25
Application and LLM security30
DevSecOps and the lifecycle15
Capstone15 to 55

Suggested mix: 30 percent concepts and demonstrations, 50 percent laboratory, 20 percent assignments and project work.

Suggested assessmentWeight
Programming assignments15
Development project15
UI and API framework15
Performance and chaos15
Security assessment15
Capstone25

Capstones

A capstone is a learner project, not a client case study. You do not name a real hospital, a vendor, or a customer. Laboratory work is repositories, a pipeline, and an isolated authorised security environment used in the programme.

OptionWhat you build
Hospital workflowRegistration, appointments, billing, role-based access, and APIs. You design and build the workflow.
Customer recordsAn enterprise customer-records workflow you build, with the roles and the records the requirement names.
RAG applicationA retrieval-augmented generation application with access control, assessed in writing on the security programme.
Microservices performanceA performance exercise with JMeter and a controlled failure in an isolated lab.

Enquire

Choose the Technology stream and the course name for the programme you want. The cohort and any certificate are confirmed at enquiry.

Enquire about AI in Software Development

Enquire about AI in Software Testing

Enquire about AI in Load Testing

Enquire about AI Security Testing

Questions

Does an AI recommendation authorise a production deployment?

No. An AI recommendation does not authorise a production deployment by itself. Every gate has evidence, a pass or fail decision, and a human owner. A person approves the release.

Which programmes does this pathway join?

Four programmes on the AI Essentials menu, not on NVIDIA AI Essentials. AI-Native Software Engineering and Development, enquired as AI in Software Development. AI-Powered Test Automation Engineering, enquired as AI in Software Testing. AI-Driven Performance and Chaos Engineering, enquired as AI in Load Testing. AI-Powered Application, API and LLM Security Testing, enquired as AI Security Testing.

How long is the pathway, and how is the work marked?

The planned shape is a 16-week professional programme, about 160 to 200 hours. That shape is subject to batch and faculty planning. The suggested split is 40 hours of software engineering and development, 35 hours of software testing and automation, 25 hours of performance and chaos engineering, 30 hours of application and LLM security, 15 hours of DevSecOps and the lifecycle, and 15 to 55 hours of capstone. The suggested mix is 30 percent concepts and demonstrations, 50 percent laboratory, and 20 percent assignments and project work. The suggested assessment mix is programming assignments 15, development project 15, UI and API framework 15, performance and chaos 15, security assessment 15, and capstone 25. The cohort and any certificate are confirmed when you enquire.

Where do classes run?

Classes run at RIA Purasawalkam and RIA Saligramam. Laboratory work is repositories, a pipeline, and an isolated authorised security environment used in the programme. Call +91 98843 63200.

Explore related RIA programmes

AI-Native Software Engineering and Development
AI-Powered Test Automation Engineering
AI-Driven Performance and Chaos Engineering
AI-Powered Application, API and LLM Security Testing
AI Essentials
AI for Business Owners

Ready to join Complete AI-Native SDLC Engineering?

Enquire on the Technology stream as AI in Software Development, AI in Software Testing, AI in Load Testing, or AI Security Testing. The cohort and any certificate are confirmed on enquiry. Call +91 98843 63200.

ENQUIRE NOW

Across every course

Seminars, workshops and faculty development

The same formats sit on any RIA subject. Name the course and the room — a faculty programme, a seminar, a workshop, an internship, or help with a project.

Faculty programme Seminar Workshop Internship Projects
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