End-to-End AI Engineering Bootcamp – Aurimas Griciunas

End-to-End AI Engineering Bootcamp by Aurimas Griciunas is an eight-week program for technical professionals working toward production AI engineering. Its sprint-based curriculum connects AI concepts with the infrastructure and practices used to ship an application.

AI engineering skills covered in the bootcamp

• Master LLM API integration with Gemini, Claude, and GPT for production applications
• Build RAG pipelines using vector databases to create AI apps that work with your data
• Learn AI agent development with LangChain, LangGraph, and Agent Development Kit
• Apply Docker and Kubernetes for containerized AI application deployment
• Develop a complete capstone project from concept to deployed production app
• Implement observability and performance testing for reliable AI systems

From AI concepts to production systems

The bootcamp focuses on the engineering work beyond experimenting with models in notebooks. It brings together application development, data retrieval, deployment infrastructure, and the practices needed to maintain an AI system after it is deployed.

The sprint-based bootcamp curriculum

The eight-week curriculum follows a sprint structure modeled on engineering workflows. Lessons introduce concepts through videos, cheatsheets, and reference code; Sprint Build Lab recordings show features being implemented in the capstone project.

The course includes hands-on work with Gemini, Claude, and GPT APIs. Its data layer covers vector databases and retrieval-augmented generation, giving applications a way to use their own data alongside model knowledge.

The agent curriculum introduces LangChain, LangGraph, and Agent Development Kit for systems that reason, plan, and carry out multi-step tasks. It also covers A2A and MCP protocols for communication between agents.

Deployment tutorials cover FastAPI, Docker, Kubernetes, and cloud infrastructure. Observability, evaluation, and performance testing address how to monitor AI applications and assess their reliability after deployment.

Capstone project and course materials

A capstone framework and templates guide the development of an application across the sprints, from the initial concept to a deployed app. The materials also include AI agent and RAG implementation guides alongside the lesson videos, cheatsheets, reference code, and build lab recordings.

About Aurimas Griciunas

Aurimas Griciunas runs Swirl AI and specializes in production AI engineering. His bootcamp curriculum draws on engineering practices used in production environments.

Who the bootcamp is for

The program is aimed at software engineers moving into full-stack AI engineering, data scientists developing deployment and infrastructure skills, and backend developers adding AI capabilities to their work. It also suits technical professionals preparing for AI engineering interviews and roles.

The curriculum assumes that learners can write Python and understand basic software development; it is not an introduction to programming.

Frequently asked questions

What does End-to-End AI Engineering Bootcamp cover?

The curriculum covers LLM APIs, retrieval-augmented generation, AI agents, deployment infrastructure, and the evaluation and monitoring of AI systems.

Who is the bootcamp designed for?

It is intended for software engineers, data scientists, backend developers, and other technical professionals preparing to work in AI engineering.

What materials are included?

The materials include sprint lesson videos, cheatsheets, reference code, Sprint Build Lab recordings, capstone templates, and implementation guides.

Do I need programming experience?

Yes. The bootcamp assumes Python proficiency and familiarity with basic software development concepts.

Who is Aurimas Griciunas?

Aurimas Griciunas runs Swirl AI and specializes in production AI engineering.