AI Software Development / AI-Native Software Development Career Program
AI-Native Software Development Career Program
From zero to AI-native software engineer — the complete journey
- Duration
- 15 months · 3 terms of 5 months
- Weekly effort
- 10–12 hours: 4 sessions + guided lab + weekly shipped project work
- Delivery
- Classroom · Online · Hybrid
- Batches
- Morning · Afternoon · Evening · Weekend
- Who can join
- 12th pass and above. No coding background required — Term 1 builds everything from zero.
You graduate with
- Public GitHub portfolio
- 3 deployed applications
- One specialization
- Placement preparation
Curriculum
The journey, term by term
Every module ends in finished work. Expand any module to see what you learn and what you build.
Term 1
Foundations of a Developer
Months 1–5
You learnThinking in code: variables, control flow, functions, data structures, files, errors — taught in Python, the world’s most versatile language. Problem-solving practice from day one. Type hints and clean-code habits from the start.
You buildDozens of working programs, ending with a menu-driven mini application — every one committed to your GitHub from week one.
You learnVersion control the professional way: commits, branches, merges, pull requests, READMEs that sell your work. Placed early on purpose — every project you build for the next 14 months gets published as you build it.
You buildYour GitHub profile — pinned projects, profile README — growing weekly for the rest of the program.
You learnThe layer most bootcamps skip: memory, pointers, compilation — taught through C. Why programs are fast or slow, what the machine actually does. This is what separates engineers from tutorial-followers in interviews.
You buildClassic C programs plus one program flashed to real hardware (ESP32) — your first taste of code touching the physical world.
You learnRelational thinking: tables, keys, joins, aggregation, window functions — on PostgreSQL. Modern reality included: JSON columns, indexing, and an introduction to vector search, the database skill of the AI era.
You buildA fully designed and queried database for a real scenario (inventory / clinic / institute), integrated with your Python programs.
You learnHTML, CSS, and JavaScript/TypeScript fundamentals — how the web actually works: requests, responses, APIs, JSON. The foundation both frontend and backend careers stand on.
You buildYour personal portfolio website — hand-built, deployed live at a public URL, linked from your GitHub.
Term exitYou think like a programmer, your GitHub is alive, and your portfolio site is on the internet.
Term 2
Full-Stack Engineering + the AI Workflow
Months 6–10
You learnPick your lane with counsellor guidance: .NET (C# + ASP.NET Core), Java (Spring Boot), or Python (FastAPI/Django). REST APIs, authentication with JWT, working with your Term 1 database through an ORM, API documentation.
You buildA complete, documented backend API for your capstone product — secured, tested, and pushed with professional commit history.
You learnModern React with TypeScript and Vite: components, hooks, state, data fetching, Tailwind styling. Connecting to your own Term 2 API. (Angular offered as an alternative for enterprise-focused students.)
You buildThe frontend of your capstone product — deployed live and talking to your own backend.
You learnThe 2026 way of working, in full: Copilot/Cursor-class assistants for generating, debugging, reviewing, refactoring, and testing code — plus the judgment layer: catching AI’s mistakes by reading, security and licensing discipline, and the rule that you own every line you ship. Includes the famous ‘trap exercise’: find the planted bug in AI-generated code before running it.
You buildOne capstone feature built twice — manually and AI-assisted — with your own written comparison of speed, quality, and defects. The interview story every company wants to hear.
You learnHow software reaches users: environment configuration, secrets done right, Docker, deploying to real hosts, HTTPS and domains, and CI/CD from GitHub — tests and deployment running automatically on every push.
You buildYour full-stack capstone LIVE on the internet with an automated pipeline — the URL goes on your resume.
You learnSoftware is a team sport: a two-week sprint in teams of 3–4 with tickets, branches, pull-request reviews, standups, and a demo — run exactly like a junior developer’s first sprint.
You buildA team-built feature shipped to a shared repository — with your reviewed pull requests as proof you can work in a team.
Term exitA deployed full-stack product, the professional AI workflow, and team experience — already at typical bootcamp-graduate level, with a term still to go.
Term 3
AI Engineering & Specialization
Months 11–15
You learnCode meets the physical world: Arduino/ESP32 sensors, Raspberry Pi, MQTT messaging, live dashboards. The bridge module to computer vision on real devices — and a skill set almost no bootcamp graduate has.
You buildA working sensor system: device → MQTT → live web dashboard, fully built and documented by you.
You learnReal ML, hands-on: scikit-learn fundamentals, then deep learning with PyTorch — transfer learning on your own images, training a custom YOLO object detector, and running it on live camera and edge hardware with honest evaluation.
You buildYour own trained detector running in real time on the Week’s hardware — camera to Raspberry Pi to dashboard. The demo that dominates interviews.
You learnBuilding with large language models as an engineer: API integration, structured outputs, tool calling, embeddings, a full RAG application over real documents, agent patterns, and where LLMs fail — hallucination handled professionally.
You buildA deployed ‘chat with your documents’ application — plus an AI assistant that answers questions about your own IoT system in plain language, connecting the whole program into one product.
You learnMentored deep-dive in your lane: Backend & Cloud · Full-Stack Product · AI/ML & Computer Vision · IoT + Edge AI. Studio-style briefs, weekly reviews, production standards.
You buildA specialization project at professional depth — the centerpiece repository of your portfolio.
You learnPolishing the portfolio: repository hygiene, READMEs, the resume for developer roles, LinkedIn/GitHub optimization, mock interviews including AI-permitted coding rounds, and application strategy for services, product companies, and freelancing.
You buildYour complete application kit + all projects presented at Graduate Demo Day before an invited audience.
Term exitThree deployed applications, a trained vision model on real hardware, a GenAI product, one specialization — and the AI-native workflow that makes you the candidate trained for how development actually works now.