Not just another Python + ChatGPT bootcamp. A complete AI engineering programme built on real projects, with the technologies companies actually ask for. From design to deployment.
A technical bootcamp for professionals moving from conventional development to AI engineering.
A structured path that mirrors how AI roles actually evolve inside companies.
Building applications on AI APIs and simple workflows.
Designing RAG systems and AI agents, cloud deployment, observability and security.
Distributed architecture, governance, cost optimisation and AI platform design.
12 progressive blocks. Every module combines theory, live coding and a hands-on project.
Static typing, advanced OOP, async programming, testing and packaging professional Python projects.
Large-scale data manipulation with the modern Python ecosystem.
Complex queries, performance tuning and relational modelling for AI.
Building robust, scalable data pipelines with industry-standard tools.
Supervised and unsupervised ML fundamentals: classification, regression, clustering and ML pipelines.
Advanced data preparation techniques to get the most out of your models.
Tensors, GPU compute, deep architectures CNN, RNN, LSTM and Transformers from scratch.
Understanding and using large language models: architectures, APIs, comparisons and use cases.
Advanced prompting techniques for reliable, structured and reproducible outputs.
Retrieval Augmented Generation: indexing, semantic search, chunking and reranking.
LLM chains, memory, tools and orchestration for complex AI applications.
Execution graphs for advanced conversational agents with state handling and branching.
MCP architecture: building tools, resources and MCP servers for IDE integration.
Collaborative multi-agent systems with the leading frameworks.
Design and expose fast, documented, production-ready AI APIs.
Securing AI applications with standard authentication and authorisation.
Real-time communication and LLM response streaming for responsive interfaces.
Building modern interfaces for AI applications with React and Next.js.
Containerising AI applications: Dockerfile, multi-stage builds, volumes and networking.
Container orchestration, autoscaling and deploying AI services on a cluster.
Infrastructure as Code to provision and manage cloud resources reproducibly.
Azure OpenAI services, Cognitive Services, Azure ML and deploying on Azure.
ML pipelines, Gemini API, Model Garden and deploying on Google Cloud.
Foundation models, SageMaker, Lambda and deploying on Amazon Web Services.
Experiment tracking, model registry, reproducibility and ML artefact versioning.
Monitoring models and LLM pipelines in production.
Automating the AI application lifecycle with GitHub Actions.
Structured logs, distributed traces and metrics to diagnose AI systems in production.
Open-source AI workflow automation with native integrations and custom nodes.
Visual automation scenarios connecting applications and AI services.
Designing intelligent workflows that combine LLMs, APIs and business logic.
Deploying AI agents on communication platforms.
The 10 critical vulnerabilities of LLM applications and how to defend against them.
Prompt injection attacks, jailbreaks and defence-in-depth techniques.
Personal data protection, anonymisation and regulatory compliance for AI systems.
Governance frameworks, the EU AI Act, bias audits and responsible AI in the enterprise.
Apply your skills to real problems across 6 industries.
Automated document analysis, risk scoring and ML fraud detection.
Smart CV matching, a recruitment assistant and HR process automation.
Contract analysis, clause extraction and AI compliance checking.
OCR of medical records, document summarisation and clinical decision support.
Predictive maintenance, anomaly detection and industrial process optimisation.
Recommendation engine, smart support chatbot and customer experience personalisation.
Each team builds a complete AI solution end to end, ready for production:
To earn the certification, every participant must:
10 bonus sessions to push your expertise further.
12 weeks, 3 modules a week. An intensive pace at roughly 15 to 20 hours a week.
Three levels of support to match your pace and your goals.
Launch prices reserved for the waiting list. They will be guaranteed to pre-registered members when the cohort opens, with no payment before.
Developers who accelerated their careers through the bootcamp.
"In 12 weeks I went from conventional backend developer to AI Engineer. The RAG + agents module landed me a role with a 30% pay rise."
"The programme is dense but extremely well structured. The MLOps/LLMOps block gave me the skills my Data Scientist profile was missing."
"The capstone project is a real differentiator. I could show a production-ready project in interviews. That is what made the difference."
Join the next cohort and build production-grade AI systems.