32 weeks. One complete path to AI engineering.
AI is part of the program from week one, not a module added at the end.
24 weeks of core training
8 weeks of residency
AI shows up in all 32 weeks. Weeks 15 to 18 are the dedicated deep dive.
Professional Git workflows, Java idioms and design patterns, algorithmic complexity, production-grade SQL/PostgreSQL, concurrency, testing, and debugging.
How Weeks 1 to 6 prepare you for AI jobs
Every AI role sits on top of solid software engineering. These six weeks cover Git, Java, SQL, testing and debugging. You also start comparing your own work with AI-assisted work, so checking AI output becomes a habit early.
- AI Engineer
- Applied AI Engineer
- Generative AI Engineer
- Forward Deployed Engineer
Spring Boot, REST APIs, authorization, testing, Docker, and backend AI-code verification.
How Weeks 7 to 10 prepare you for AI jobs
AI features reach users through backend services. You build Spring Boot APIs with authorization and tests, package them in Docker, and review AI-generated backend code at Week 10. It is the base for AI Engineer work and for the customer integrations a Forward Deployed Engineer builds.
- AI Engineer
- Generative AI Engineer
- Forward Deployed Engineer
React, TypeScript, state management, API integration, UI fundamentals, and frontend verification.
How Weeks 11 to 14 prepare you for AI jobs
Users meet AI through a screen. You build React and TypeScript interfaces that call APIs and handle real data, and at Week 14 you explain every change AI made to your code. That is what it takes to ship an AI feature a customer can use.
- Generative AI Engineer
- Forward Deployed Engineer
- Applied AI Engineer
Applied Python, AI tools, prompting-to-architecture workflows, RAG and LLM foundations, and AI verification.
How Weeks 15 to 18 prepare you for AI jobs
This is the phase closest to AI roles. You learn applied Python, RAG and LLM foundations, and how to go from a prompt to a designed system, then defend your verification work at Week 18. It covers LLM foundations, not deep model training, so Machine Learning Engineer roles need more study.
- AI Engineer
- Applied AI Engineer
- Generative AI Engineer
AWS, deployment, IAM, CI/CD, monitoring, security scanning, and incident response.
How Weeks 19 to 20 prepare you for AI jobs
An AI feature is only useful if it keeps running. You deploy on AWS, set up IAM and CI/CD, add monitoring and security scanning, and practise incident response. AI engineering roles involve shipping and supporting systems, not only building prototypes.
- AI Engineer
- Applied AI Engineer
- Forward Deployed Engineer
Requirements, architecture, team delivery, deployment, and defense.
How Weeks 21 to 24 prepare you for AI jobs
You take a project from requirements to architecture, build it with a team, deploy it and defend it, and you hand in a record of how you used AI. What you get is evidence an employer can check. It is also the loop a Forward Deployed Engineer repeats for every customer.
- Forward Deployed Engineer
- AI Engineer
- Applied AI Engineer
- Generative AI Engineer
Supervised engineering work with staged access based on readiness and approval.
How Weeks 25 to 32 prepare you for AI jobs
You work on supervised assignments with mentoring and code reviews, and your access widens as you show readiness. Selected students may get supervised exposure to Centizen, ZenBasket, Zenyo or approved client work. You practise delivering, supporting and explaining real work with AI tools in the loop.
- Forward Deployed Engineer
- Applied AI Engineer
- AI Engineer
AI verification checkpoints
At each checkpoint you explain, verify and defend the work AI helped you build.
| Week(s) | Checkpoint |
|---|---|
| Week 6 | Compare your work with and without AI, and verify the result (end of Software Engineering Core) |
| Week 10 | Review AI-generated backend code and correct its mistakes |
| Week 14 | Explain and defend every AI-assisted change in your frontend code |
| Week 18 | Oral defense of how you verified your AI-assisted work |
| Weeks 21 to 24 | A record of where and how you used AI, submitted with your capstone |
| Weeks 25 to 32 | Verification of your AI use in real engineering work, assessed during the residency |
Security across the program
Security is taught inside the work, not as a standalone course.
| Area | Security content |
|---|---|
| Database (Week 4) | Permissions, injection protection, sensitive data handling |
| Backend (Week 8) | Auth, authorization, input validation, OWASP basics |
| Docker and cloud (Weeks 10 and 19) | Secrets management, IAM, least privilege |
| Frontend (Week 13) | Token handling, XSS/CSRF awareness |
| CI/CD (Week 20) | Dependency scanning, secrets scanning |
| Capstone | Simple threat model plus security checklist |
Engineering includes communication.
Students learn to: clarify ambiguous requirements, document assumptions, explain technical trade-offs, write architecture and design notes, create runbooks, provide asynchronous status updates, demonstrate completed work, accept and respond to feedback, and communicate with technical and non-technical stakeholders.
This is graded. It is woven through the capstone build and the residency, not added on as a soft-skills module.
Structure
Five days a week, defined hours, attendance tracked, with regular progress updates.
Applications are open for AI-Native Software Engineering
25 seats. Starting January 2027 in Tirunelveli.
Rs 1,50,000(inclusive of GST), Exclusive price