This course is popular
4 people enrolled last week.
🚀 Build Real AI Products, Not Just Prototypes
The End-to-End AI Engineering Bootcamp is an 8-week, cohort-based experience designed to turn technical professionals into full-stack AI engineers who can confidently design, build, and deploy production-grade AI systems.
🛠️ What You’ll Build
You’ll develop your own capstone project - a real-world AI application built sprint by sprint, applying each week’s concept to solve a business-relevant use case. By the end, you’ll present it live on Demo Day, with a working repo and deployed app you can showcase to hiring managers, CTOs, or investors.
🧑💻Technologies include:
✔️ LLM APIs (Gemini, Claude, GPT, etc.).
✔️ Vector databases & RAG.
✔️ AI agent libraries (LangChain, LangGraph, ADK).
✔️ Docker, FastAPI, Kubernetes, cloud deployment.
✔️ Observability, evaluation, and performance testing.
✔️ Modern communication protocols (A2A, MCP).
🧠 How It Works
Each week follows a real engineering sprint:
Sprint Lesson (Monday): Self-paced learning with videos, cheatsheets & reference code.
Sprint Review (Tuesday): Live walkthrough with Aurimas + deep Q&A.
Sprint Build Lab (Thursday): Live coding session to implement key sprint features.
Bonus QnA and Feedback sessions.
What you’ll learn
Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.
Learn how to systematically evaluate and improve RAG based systems.
Apply techniques like Hybrid Retrieval (BM25 + Dense Embeddings) and Reranking to optimise Retrieval process of your RAG Systems.
Utilize synthetic data generation to help you improve the system without needing real user data.
Create agents that can plan steps, use tools and complete tasks on their own.
Evolve your RAG into Agentic RAG System to support complex user queries grounded in context from different data sources.
Connect your Agentic Systems to tools via MCP.
Learn patterns for designing Multi-Agent Systems and how to add safeguards so that they act predictably.
Implement A2A (Agent to Agent) protocol to allow your agents to communicate with other remote agents.
Learn to use structured outputs so the model’s responses fit cleanly into downstream systems.
Apply best practices for prompt versioning and evolution.
Learn how to Evaluate GenAI applications of different complexities.
Implement Eval Quality Gates as part of your CI/CD pipeline.
Add Observability to your systems from the first week.
Set up APIs and services so they run reliably in production.
Deploy your application to the cloud and expose it to potential users.
Learn directly from Aurimas
Aurimas Griciunas
LinkedIn Top Voice in AI • Founder & CEO @ SwirlAI • Former CPO @ Neptune.ai
Who this course is for
Data Professionals (Analysts & Scientists)
Looking to move beyond analysis and modeling to build and deploy real-world AI systems.
ML Engineers
Who want to deepen GenAI skills and master scalable, production-ready AI engineering from end to end.
Data Engineers
Ready to expand into AI by learning how to integrate data pipelines with LLMs, RAG, and agent-based systems.
What's included
Live sessions
Learn directly from Aurimas Griciunas in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Code-along Recordings
30+ Hours of pre-recorded coding videos that you can follow while building out your Capstone.
Extensive Reading Materials
200+ Pages of reading material that you can refer to during and after the Bootcamp.
Compute Credits
$500 in Modal Compute Credits.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
21 live sessions • 47 lessons • 8 projects
Week 1
Jan
13
Sprint Review: Project framing, tooling overview, and repo setup
Tue 1/134:00 PM—6:00 PM (UTC)Jan
15
Sprint Build Lab: Set up development environment and scaffold project repo
Thu 1/154:00 PM—6:00 PM (UTC)
Jan
12
End-to-end AI Engineering bootcamp Prep
Sprint 0 – Problem Framing & Infrastructure Setup
Jan
16
Office Hours
Week 2
Jan
20
Sprint Review: Walkthrough of RAG structure and MVP objectives
Tue 1/204:00 PM—6:00 PM (UTC)Jan
22
Sprint Build Lab: Implement and evaluate your first end-to-end RAG pipeline
Thu 1/224:00 PM—6:00 PM (UTC)
Sprint 1 – Build the First Working RAG Prototype
Free lesson
.png&w=768&q=75)
Deploy Reliable AI Systems with LLMOps
What Is LLMOps
Learn what LLMOps is and why it’s essential for production-ready LLM applications.
Build Observability into AI Systems
Learn how to evaluate and monitor LLM-based systems to detect failures before they reach users.
Build Your Roadmap
Create a clear step-by-step LLMOps plan that fits your team’s tools, workflows, and stage of AI adoption.
Schedule
Live sessions
5 hrs / week
Mon, Jan 12
4:00 PM—5:00 PM (UTC)
Fri, Jan 16
4:00 PM—5:00 PM (UTC)
Fri, Jan 30
4:00 PM—5:00 PM (UTC)
Projects
7 hrs / week
Async content
5 hrs / week
Frequently asked questions
$1,900
USD