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Flagship career programme · Advanced

AI & Generative AI Engineer Career Program

Build and deploy production-grade AI systems spanning ML, deep learning, GenAI, RAG, agents and MLOps.

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School of Artificial Intelligence learning pathway
Python + AI FoundationsMachine Learning EngineeringDeep Learning + VisionGenerative + Agentic AI
Python 3VS CodeJupyterGitGitHub
Duration9–12 months
Batch startsConfirm with admissions
Learning formatLive mentor-led learningConfirm cohort format with admissions
Curriculum8 phasesIncluding a flagship capstone
Portfolio4 portfolio examples
LevelAdvancedProgramme level
PathwayAI Engineer / GenAI Engineer / ML EngineerRelated career pathway

How you will learn

Live classes
Labs
Projects
Capstone
Mentor feedback
Career preparation
Programme curriculum

What you will learn, phase by phase

Explore the eight-phase pathway and its related syllabuses. Some phases use selected topics or alternative course options.

01Phase 1Python + AI FoundationsBuild programming, data and AI foundations.
Topics you will cover
  • Python Setup and Developer Workflow
  • Core Python Programming
  • Data Structures and Pythonic Problem Solving
  • Object-Oriented Python and Packaging
  • NumPy, Pandas and Data Preparation
  • Math and Statistics for AI
  • Machine Learning Workflow Foundations
  • AI Foundations Capstone
02Phase 2Machine Learning EngineeringEngineer reliable supervised/unsupervised ML solutions.
Topics you will cover
  • ML Problem Framing and Experiment Design
  • Feature Engineering and Data Pipelines
  • Regression Engineering
  • Classification Engineering
  • Unsupervised Learning and Representation
  • Model Evaluation, Tuning and Explainability
  • Packaging, APIs and Production Inference
  • Monitoring, Responsible ML and Capstone
03Phase 3Deep Learning + VisionBuild advanced neural and computer-vision systems.
Topics you will cover
  • Neural Network Foundations
  • Deep Learning Engineering Workflow
  • CNNs and Transfer Learning
  • Object Detection and Segmentation
  • Vision Transformers and Embeddings
  • Multimodal AI and Vision-Language Systems
  • Optimization, Evaluation and Edge/Cloud Deployment
  • Computer Vision Engineering Capstone
04Phase 4Generative + Agentic AIBuild RAG, tool-using and agentic AI applications.
Topics you will cover
  • LLM and Generative AI Engineering Foundations
  • Prompt and Context Engineering
  • Embeddings, Vector Search and Knowledge Ingestion
  • RAG Engineering and Retrieval Quality
  • Tool Use, Function Calling and API Integration
  • Agentic AI, Memory and MCP-Style Integrations
  • Multi-Agent Workflows and Enterprise Orchestration
  • Evaluation, Guardrails and Responsible GenAI
  • Production Deployment, Observability and Cost
  • Generative and Agentic AI Capstone
05Phase 5MLOps + LLMOpsOperationalise ML and GenAI with production controls.
Topics you will cover
  • MLOps and LLMOps Operating Model
  • Experiment Tracking and Reproducibility
  • Data and Training Pipelines
  • CI/CD/CT for ML Systems
  • Model Serving and Scalable Inference
  • LLMOps: Prompt, Model and Knowledge Lifecycle
  • Monitoring, Drift and AI Observability
  • Governance, Security and Production Capstone
06Phase 6Cloud + DevOps IntegrationContainerise, automate and deploy AI systems using modern platform practices.
Topics you will cover
  • DevOps Foundations, Git and Flow of Work
  • Continuous Integration and Artifact Management
  • Docker and Container Engineering
  • Kubernetes Application Platform
  • Helm, GitOps and Release Engineering
  • Infrastructure as Code and Cloud Automation
  • Observability, SRE and Incident Readiness
  • DevSecOps and Software Supply Chain
  • Platform Engineering and Developer Experience
  • DevOps and Cloud Platform Capstone
07Phase 7Industry SprintComplete a domain sprint in healthcare, finance, retail, life sciences or enterprise operations.
08Phase 8Flagship CapstoneDeliver a production-grade enterprise AI copilot/agent with measurable quality and business value.
Applied portfolio

Portfolio examples and flagship capstone

Explore project examples and the integrated flagship capstone. Confirm your cohort’s project selection with admissions.

Portfolio example 1

Customer Churn Starter AI

Clean customer data, engineer basic features, train a baseline classifier and explain results.

Data cleaning notebook · model notebook · README · metric summary · 5-minute demo.
Portfolio example 2

Production Churn Prediction API

Train, tune, explain and deploy a churn classifier as an API.

ML pipeline · model card · FastAPI endpoint · Dockerfile · monitoring plan.
Portfolio example 3

Visual Defect Detection

Detect defects from product/industrial images and deploy inference.

Dataset strategy · model · evaluation · inference API · demo.
Portfolio example 4

Enterprise Knowledge Copilot

RAG assistant over approved enterprise documents with citations, evaluation and guardrails.

Ingestion pipeline · retrieval benchmark · app · traces · evaluation report.
Flagship capstone

Enterprise AI Copilot & Agent Platform

Learners integrate the complete pathway into a production-style capstone with a clear brief, architecture or process design, working implementation, testing or evaluation evidence, documentation, mentor review and a final technical or business presentation.

Project brief and architecture or process design. · Working implementation and testing or evaluation evidence. · Documentation and final technical or business presentation.
Programme value

Why this programme

Become the engineer who can take AI from data and models to GenAI applications, agents and production operations.

Programme fit

Who this programme is for

Students, developers, analysts, data professionals and working technologists targeting AI, machine learning, Generative AI or LLM application roles.

AdvancedAI Engineer / GenAI Engineer / ML Engineer
PrerequisitesNo advanced AI experience is required. The pathway begins with Python and AI foundations; consistent coding practice is essential.
Practical capabilities

What you will be able to do

  • Build programming, data and AI foundations.
  • Engineer reliable supervised/unsupervised ML solutions.
  • Build advanced neural and computer-vision systems.
  • Build RAG, tool-using and agentic AI applications.
  • Operationalise ML and GenAI with production controls.
  • Containerise, automate and deploy AI systems using modern platform practices.
  • Complete a domain sprint in healthcare, finance, retail, life sciences or enterprise operations.
  • Deliver a production-grade enterprise AI copilot/agent with measurable quality and business value.
Tools and platforms

Technology you will use in this programme

Python 3VS CodeJupyterGitGitHubpytestNumPypandasSciPyscikit-learnMLflowimbalanced-learnXGBoostLightGBMUMAPOptunaSHAP
Career relevance

AI Engineer / GenAI Engineer / ML Engineer

Prepare for AI Engineer, GenAI Engineer and ML Engineer roles through practical skills, project evidence and interview preparation. Employment is not guaranteed.

Programme evidence and instruction

Programme guidance

Discuss your learning pathway

Review prerequisites, learning format and project expectations with admissions before enrolment.

Get programme guidance
Project evidence

Explore the programme projects

Review the project briefs and deliverables to understand the work expected during the programme.

Review project expectations
Programme FAQs

Clear answers before you enrol

How is a flagship programme different from a focused course?

A focused course develops one defined capability. A flagship programme combines foundations, specialist skills, production practices, capstone work and career preparation into a longer role-based journey.

How much time should I plan each week?

Confirm live class times and the expected weekly commitment with admissions before enrolment.

What practical work will I complete?

The curriculum includes guided labs, project work and a flagship capstone. Review the portfolio examples above and confirm cohort-specific project requirements with admissions.

How will my progress be assessed?

Progress is reviewed through guided labs, project work, implementation and evaluation evidence, and a final capstone presentation.

Are vendor certifications included?

The curriculum develops practical skills. Vendor certification exams and official certification status are not included unless explicitly confirmed in your enrolment offer.

Does this programme guarantee a job?

No. Employment depends on learner performance, experience, hiring conditions and employer decisions.

Will I receive a certificate?

A programme completion certificate may be issued after you meet the published attendance, assignment, project and assessment requirements.

Ready to start?

Ready to explore the AI & Generative AI Engineer pathway?

Review the curriculum and confirm prerequisites, fees and the next available cohort with admissions before enrolment.

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