Corporate AI program
AI Engineering Training for Developers
For: Software developers, data scientists, ML engineers and architects moving into GenAI.
In short
AI Engineering Training turns software developers into engineers who can build production generative AI applications — working with LLM APIs, retrieval-augmented generation (RAG), vector databases, fine-tuning, agents, evaluation and deployment. It is a build-first bootcamp taught by practitioners behind 300+ open-source AI projects.
What your team will be able to do
- Build LLM applications with the OpenAI, Anthropic, Gemini and open-model APIs
- Design RAG pipelines that are accurate, cited and evaluated
- Choose between prompting, RAG, fine-tuning and agents
- Deploy, monitor and cost-control GenAI services
- Deliver a production-shaped GenAI application as a team
Capstone
Teams build and deploy a GenAI application — typically a RAG assistant or an agentic workflow on company documents — with evals and a monitoring dashboard.
Tools & stack
Curriculum
Program modules
A typical outline — every program is tailored to your team's roles, tools, data policies and use cases after a scoping call.
- 01
LLM foundations for engineers
- Transformers, tokens, sampling and context
- APIs, streaming, structured output
- Open models: Llama, Qwen, Mistral, Gemma; running locally with Ollama/vLLM
- 02
Retrieval-augmented generation
- Chunking, embeddings and vector databases
- Hybrid search, re-ranking and query rewriting
- RAG evaluation: faithfulness, relevance, recall
- 03
Beyond RAG
- Fine-tuning with LoRA/QLoRA — when it's worth it
- Multimodal: documents, images, speech
- Agents and tool use
- 04
LLMOps
- Evals and regression testing
- Guardrails, PII redaction and prompt-injection defence
- Tracing, latency and cost optimisation
- 05
Shipping
- FastAPI services, containers, cloud deployment
- Azure OpenAI, AWS Bedrock, Google Vertex AI
- Security reviews and rollout
FAQs
AI Engineering: frequently asked questions
Still have questions? Talk to our team — we reply within one working day.
What does an AI engineering training program cover?
LLM fundamentals, working with model APIs, retrieval-augmented generation, vector databases, fine-tuning, agents, evaluation, guardrails and deployment — with hands-on labs at every step and a team capstone.
Is this suitable for Java or .NET developers?
Yes. Labs are in Python by default, but we cover the concepts language-agnostically and can adapt examples to Java (Spring AI, LangChain4j) or .NET (Semantic Kernel).
Can you run AI engineering training on our cloud?
Yes. We regularly run labs on Azure OpenAI, AWS Bedrock and Google Vertex AI, or on-prem open models, so teams learn on the stack they'll ship on.
How large can an AI engineering batch be?
For hands-on bootcamps we recommend 20–35 engineers per batch with a trainer and teaching assistant. Larger groups run as parallel batches.
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Next step
Bring AI Engineering to your team.
Tell us who you want to upskill and what outcome you need. You'll get a tailored proposal — curriculum, format, trainers and pricing — within 48 hours.