Train the people who will run your AI.
Hands-on programmes for engineering teams, taught by practitioners who build production systems. Run for a single client team or alongside one of our engagements.
Applied AI Engineering
How to build AI systems that hold up in production: retrieval, agents, evaluation and the guardrails around them.
Who it is for
Software engineers who will build and maintain LLM-based systems.
How it runs
Project-based. Participants work on a system that resembles their own, reviewed by mentors who have built the real thing.
Modules
- How LLMs behave: tokens, context, failure modes
- Retrieval-augmented generation: chunking, embeddings, vector databases, re-ranking
- Working with Bangla and mixed-language text
- AI agents and tool use, with human approval points
- Evaluation: building test sets and measuring quality
- Guardrails, security and prompt-injection defence
- Deployment, monitoring and cost control
- Capstone: a production-grade system on your own data
Cloud-Native Engineering for AI
The platform skills AI systems depend on: Go and Rust services, event-driven design and Kubernetes.
Who it is for
Experienced Java, C# or C++ engineers moving to modern platforms.
How it runs
Project-based. Participants work on a system that resembles their own, reviewed by mentors who have built the real thing.
Modules
- Go for backend engineers
- Rust fundamentals and where it pays off
- Service design: modular monoliths and microservices
- Event-driven architecture with Kafka
- Containers and Kubernetes in practice
- Observability: logs, metrics, tracing
- Data pipelines that feed AI systems
- Capstone: modernising a legacy component
DevSecOps Masterclass
Security built into every stage of delivery, including the parts that are new with AI.
Who it is for
Engineering, operations and security teams in regulated organisations.
How it runs
Project-based. Participants work on a system that resembles their own, reviewed by mentors who have built the real thing.
Modules
- Secure CI/CD pipelines
- Dependency and supply-chain security
- Secrets management and access control
- Infrastructure as code with policy checks
- Security for AI systems: data leakage, prompt injection, model access
- Audit trails your regulator can follow
Planning to train a team?
Tell us how many engineers, what they work on today and what you want them to build. We will suggest a programme and format.
Ask about team training