End-to-End AI Engineering Bootcamp (Maven)

Learn how to build, deploy, and scale production-grade AI applications using large language models, RAG systems, agent frameworks, vector databases, and modern AI engineering practices.

Many developers learn AI through tutorials that focus on simple API calls and prototypes.

While these projects may work in demonstrations, they often fail to address the challenges involved in deploying reliable, scalable AI systems in real-world environments.

End-to-End AI Engineering Bootcamp by Aurimas Griciunas is designed to bridge the gap between AI theory and production-ready software engineering.

The program focuses on building practical AI applications using modern engineering workflows, advanced retrieval systems, multi-agent architectures, LLMOps, deployment infrastructure, and scalable backend systems.

Key Benefits

✅ Learn how to build production-ready AI applications

✅ Develop advanced RAG systems and retrieval pipelines

✅ Build AI agents and multi-agent workflows

✅ Master modern AI engineering practices

✅ Learn vector database implementation

✅ Understand LLMOps and observability systems

✅ Deploy AI applications with confidence

✅ Build scalable backend architectures

✅ Learn real-world AI product development workflows

✅ Improve AI application reliability and performance

✅ Create portfolio-ready AI engineering projects

✅ Gain practical experience through hands-on development

What You'll Learn

AI Engineering Foundations

• Understanding AI application architecture
• Modern AI development workflows
• Building production-focused systems
• AI engineering best practices

Large Language Model Applications

• Working with LLM-powered products
• Prompt engineering fundamentals
• Model integration techniques
• Application design principles

Retrieval-Augmented Generation (RAG)

• Building advanced RAG systems
• Retrieval pipeline design
• Document processing workflows
• Improving response quality and accuracy

Vector Databases

• Understanding vector search
• Embedding generation concepts
• Vector database implementation
• Similarity search optimization

Multi-Agent Systems

• Agent architecture fundamentals
• Multi-agent orchestration techniques
• Agent communication workflows
• Task coordination systems

Backend Development for AI

• API development fundamentals
• FastAPI implementation
• Service architecture design
• Backend scalability principles

LLMOps & Observability

• Monitoring AI applications
• Tracking model performance
• Observability frameworks
• Reliability and maintenance strategies

Data Processing Pipelines

• Data ingestion workflows
• Data transformation systems
• Knowledge base creation
• Information retrieval optimization

Docker & Containerization

• Containerized AI deployments
• Environment management
• Deployment workflows
• Application portability

Kubernetes Fundamentals

• Orchestrating AI services
• Scaling infrastructure
• Managing deployments
• Cloud-native architecture concepts

Production AI Deployment

• Launching AI applications
• Managing production environments
• Security and reliability considerations
• Scaling AI systems effectively

Capstone Project Development

• Building an end-to-end AI application
• Applying engineering best practices
• Creating a production-ready portfolio project
• Solving real-world business problems with AI

Course Features

• Structured bootcamp curriculum
• Hands-on engineering projects
• Live build labs
• Weekly engineering sprints
• Production AI application development
• RAG implementation training
• Multi-agent system workshops
• LLMOps and observability training
• Capstone project experience
• Real-world deployment workflows

Who It's For

• Software engineers
• Machine learning engineers
• AI developers
• Backend developers
• Full-stack developers
• Data scientists
• Technical founders
• Product engineers
• Developers building AI applications
• Anyone looking to become a production-ready AI engineer