13+ Modules
Comprehensive curriculum from Python basics through agentic systems
Developer track
Master AI & Generative AI Development
From Python fundamentals to production-ready AI agents, RAG systems and cloud deployment — built project by project with mentors who ship AI for a living.
- 13+ Modules
- 6 Portfolio Projects
- Cloud Deployment
- Career Support
Program highlights
What the track covers — and what you leave with
A complete path through the modern AI developer stack, taught hands-on and finished with a portfolio you can show.
Production-Ready
Build and deploy AI applications using industry-standard tools
LLMs, RAG & Agents
Master the latest GenAI architectures, frameworks and patterns
Career Guidance
Portfolio development, resume optimisation and hands-on job assistance
6–8
weeks, live + recorded
Who it's for
Built for people who already work with software
Developers, testers, analysts, data engineers and architects who want to move from using AI tools to building with them.
- Work with LLMs, RAG and AI agents in production patterns
- Use cloud AI services and modern AI tooling with confidence
- Ship a portfolio-grade project you can demo in interviews
- Learn live with mentors, rewatch every recorded session
The curriculum
A complete curriculum, Python to production
Open any module to see its topics. The tools you'll use are listed under each title.
Where generative AI sits inside AI as a whole, what the current model landscape offers, and how to judge which tool fits a task. Sets the vocabulary and the responsible-use ground rules for the track.
- Foundations of AI (ML, DL, NLP)
- GenAI Ecosystem & LLM Overview
- AI Tools Landscape (ChatGPT, Claude, Gemini)
- Prompt Engineering Introduction
- Ethical AI & Responsible Use
- Choosing a model for a task
Projects
Production-ready projects you'll build
Each project uses the tools from the modules before it, so your portfolio grows as you learn.
AI Knowledge Assistant with RAG
Build a document Q&A bot using LangChain, OpenAI, and vector databases
LangChain
FAISS
OpenAI
Multi-Agent Research System
Create collaborative AI agents that plan, research, write, and review
CrewAI
LangGraph
Cloud-Deployed Chatbot
Deploy a production chatbot with Docker, FastAPI, and cloud services
Docker
FastAPI
AWS/GCP
Agentic Workflow Automation
Build an autonomous system for email summarization and task management
AutoGen
n8n
Custom GPT Applications
Design and deploy specialized GPTs for business use cases
OpenAI GPTs
Gemini Studio
Capstone: Full AI Pipeline
End-to-end project: data ingestion → model → agents → cloud deployment
Full Stack
Portfolio Ready
Tools & technologies
The stack you'll master
From the first notebook to a deployed, monitored service.
Languages & Libraries
Python
Pandas
NumPy
TensorFlow
PyTorch
Keras
LLM APIs & Frameworks
OpenAI API
Claude API
Gemini API
LangChain
LangGraph
Agent Frameworks
CrewAI
AutoGen
FAISS
Pinecone
Chroma
Cloud & DevOps
AWS
Azure
GCP
Docker
Kubernetes
After the program
Roles you'll be ready for
AI Developer
Build AI-powered production apps
Data Scientist
Analyse data, build models
Prompt Engineer
Design reliable LLM prompts
MLOps Engineer
Deploy ML systems reliably
Agent Specialist
Design reliable multi-agent systems
Common Questions
