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End-to-End AI Engineering

Advanced

The End-to-End AI Engineering Bootcamp is an 8-week, cohort-based experience designed to transform technical professionals into full-stack AI engineers. Gain hands-on experience with RAG, agentic systems, and scalable deployments, and leave with a working AI product ready to showcase.

RAG systems
AI agents
Prompt engineering
Cloud deployment
Containerization

Overview

This bootcamp offers a comprehensive journey into AI engineering, focusing on building production-grade AI systems. Participants will learn to design, build, and deploy AI applications using cutting-edge technologies like LLM APIs, vector databases, and AI agent libraries. The course emphasizes practical skills, enabling learners to create a capstone project that demonstrates their ability to solve real-world business challenges.

English
course language
Professional Certification
upon course completion

Who is this course for?

Data Professionals

Analysts and scientists looking to move beyond analysis and modeling to build and deploy real-world AI systems.

ML Engineers

Engineers who want to deepen GenAI skills and master scalable, production-ready AI engineering from end to end.

Data Engineers

Engineers ready to expand into AI by learning how to integrate data pipelines with LLMs, RAG, and agent-based systems.

This bootcamp equips learners with the skills to build and deploy production-grade AI systems, covering key topics like RAG, AI agents, and cloud deployment. Ideal for data professionals and engineers, it prepares participants to advance their careers by showcasing a fully operational AI application.

Pre-Requisites

1 / 3

Foundational Python knowledge
Basic understanding of machine learning
Experience with data analysis

What will you learn?

Week 1: Prep Sprint 0 – Problem Framing & Infrastructure Setup

Project framing, tooling overview, and repo setup. Set up development environment and scaffold project repo.

Week 2: Sprint 1 – Build the First Working RAG Prototype

Walkthrough of RAG structure and MVP objectives. Implement and evaluate your first end-to-end RAG pipeline.

Week 3: Sprint 2 – Retrieval Quality & Prompt Engineering

Evaluation methods and automated prompt tuning. Improve context retrieval, prompts, and system robustness.

Week 4: Sprint 3 – Moving From Basic To Agentic RAG

Moving from basic to agentic RAG. Build a tool-using agent integrated with your RAG backend.

Week 5: Sprint 4 – Agents & Agentic Systems

Autonomous agents. Build agentic systems.

Week 6: Sprint 5 – Multi-Agent Systems

Designing and orchestrating multi-agent workflows. Implement a multi-agent task flow and run coordination scenarios.

Week 7: Sprint 6 – Deployment, Optimization and Reliability

Best practices for cloud deployment, monitoring, and performance tuning. Containerize your capstone and implement CI/CD.

Week 8: Sprint 7 – Final Demo & Capstone Delivery

Present your working AI product to cohort. Closing celebration and feedback.

Meet your instructor

Aurimas Griciūnas
Instructor, SwirlAI
Aurimas Griciūnas is a recognized AI expert, LinkedIn Top Voice in AI, and the founder of SwirlAI. He previously served as Chief Product Officer at Neptune.ai where he worked closely with top ML teams to scale infrastructure, evaluation, and LLMOps practices across industries.

New cohorts coming soon