Maven – End-to-End AI Engineering Bootcamp

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.
Design and optimize RAG architectures
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.
Engineer and orchestrate agentic systems
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.
Design and deploy multi-agent systems for complex workflows
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.
Implement evaluation strategies targeting multi-agent systems.
Implement structured prompt and context management
Learn to use structured outputs so the model’s responses fit cleanly into downstream systems.
Apply best practices for prompt versioning and evolution.
Apply LLMOps for observability and continuous evaluation
Learn how to Evaluate GenAI applications of different complexities and architectures.
Implement Eval Quality Gates as part of your CI/CD pipeline.
Add Observability to your systems from the first week.
Build and deploy production-grade GenAI applications
Set up APIs and services so they run reliably in production.
Deploy your application to the cloud and expose it to potential users.