Free consultation call booked!

Topics

• Deep Learning Fundamentals

• Transformer architecture overview

• LLM lifecycle (training, fine-tuning, inference, deployment)

• Python + key AI engineering tools setup

Hands-on

• Build your first LLM prompt pipeline

• Explore OpenAI, Azure OpenAI & HuggingFace APIs

Topics

• RAG pipelines end-to-end

• Query routing, document chunking

• Combining multiple data sources (PDFs, websites, DBs)

• LangChain RAG basics

Hands-on

• Build a production-ready RAG chatbot

• Implement LangChain RAG with structured + unstructured data

Topics

• Why LLM apps fail: hallucinations, drift, incorrect grounding

• Monitoring LLMs (LangSmith, Weights & Biases)

• Evaluation metrics: BLEU, ROUGE, grounding score, latency, cost

Hands-on

• Build 5 evaluation tests on your RAG system

• Add LangSmith logging + traces

Topics

• Building AI systems using text, images, audio

• Agents vs multi-agents

• Tools, tool calling, memory, planning

Hands-on

• Build an agent that uses tools (web search, calculator, workflow)

• Build a multi-agent problem solver (planner + worker agents)

Topics

• Architecting large LLM apps

• RAG + Agents + Observability integration

• Deployment fundamentals (FastAPI + Azure)

Hands-on

• Full end-to-end project (students choose one):

  • Customer-support chatbot

  • Document Q&A system

  • AI agent workflow automation

• Deploy on Azure / Render

Topics

• Azure Cognitive Services

• Vision, OCR, translation, speech, and text services

• Azure Machine Learning basics

• Responsible AI

Hands-on

• Deploy Vision & OCR services

• Build a pipeline using Azure AI Studio

Topics

• Designing AI-102 compliant solutions

• Prompt flow & orchestration

• AI application security + cost optimisation

• Practice mock exam (full-length)

Hands-on

• Build an Azure-hosted end-to-end AI solution

• 3 x timed mock tests

• Final revision bootcamp

Students must:
✔ Sit the Microsoft AI-102 Exam
✔ Submit certification completion proof
✔ Get certified before starting internship

• Exam tips & tricks
• Mock AI102 exam

Internship onboarding:

• Meet your supervisor

• Understand business problem

• Define scope of the AI feature to build

Tasks

• Requirements documentation

• Dataset or API exploration

• System architecture for AI feature

• Milestone: Architecture + plan approved

Examples of Projects

• RAG chatbot for internal documents

• AI assistant for customer support

• AI-powered workflow automation

Tasks

• Develop a functional prototype

• Integrate RAG, agents, or multi-modal components

• Add monitoring + evaluation (LangSmith)

Milestone

• MVP ready, tested internally

• Exam tips & tricks
• Mock AI102 exam

Tasks

• Final demo to the start-up

• Handover documentation

• Add project to resume (we help you write it)

• Add project to LinkedIn + GitHub

Outcome
✔ Practical Australian experience
✔ AI-102 Certified
✔ Full AI Engineering portfolio item