Transform Your Team into Production-Ready AI Engineers
Build the internal engineering capability to deploy your own AI models securely and at scale with the AI Engineer apprenticeship.
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Empowering AI Transformation at Industry Leaders
A Curriculum Engineered for Production
We combine advanced theory with the industry-standard tooling your team needs to ship secure, scalable AI products.
Python
PyTorch
Docker
Git
Cloud
Jupyter/MLflow
OpenAI
Foundations of Machine Learning Engineering
Master the software engineering fundamentals required for AI, including clean code principles and data preparation.
Practical Machine Learning Techniques
Build and validate supervised/unsupervised models that solve real business problems, focusing on accuracy and tuning.
Product Management for AI
Align AI projects with business objectives. Learn scoping, stakeholder management, and design thinking.
Neural Networks and Deep Learning
Design and optimise advanced architectures for complex tasks where traditional ML falls short.
Explainability and Interpretability
Open the "black box." Communicate AI decisions transparently to stakeholders and regulators.
AI Ethics and Governance in Practice
Navigate the regulatory landscape. Implement frameworks for data security, privacy, and bias mitigation.
Generative AI and Large Language Models
Leverage LLMs and Transformers for commercial applications like RAG chatbots and automation.
Hackathon
Rapidly prototype and demo a working AI solution in a collaborative, time-constrained environment.
Machine Learning in Production
The core of engineering. Transition prototypes to live environments using MLOps best practices.
Securing and Optimising ML Systems
Ensure your models are scalable, sustainable, and secure against adversarial attacks.
Secure, Browser-Based Engineering Sandbox
Learners practice engineering workflows in an isolated environment that mirrors a standard production stack (VS Code, Terminal). It runs entirely in the browser, eliminating local setup time and security risks.
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Isolated Execution: Practice safely in secure containers without risking live infrastructure or data.
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Zero Local Config: Tools (Python, Docker, Git) are pre-installed in the browser. No admin rights required.
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Automated Validation: Instant, automated feedback on code quality and logic.
Suitable Roles & Strategic Capability
The Level 6 AI Engineer programme is your pipeline for enterprise-grade AI expertise.
Core AI & ML Engineering
AI Engineer / Machine Learning Engineer candidates focusing on designing, training, deploying, and maintaining production-ready ML models.
Data & AI Product Roles
AI Product Engineer and Applied Data Scientists who bridge business challenges with technical solutions, translating requirements into commercially valuable predictive models.
Specialised & Progression Paths
MLOps Engineer focusing on optimising production pipelines, automation, and system security, or AI Solutions Engineers managing complex client or internal requirements.