AI System Design for Engineers and Interviews

About this course

Most engineers can build AI applications.

Very few engineers can design AI systems that scale.

As AI adoption grows, companies need engineers who understand not just models and APIs, but also the architecture, scalability, reliability, and infrastructure behind production AI systems.

This course focuses entirely on AI System Design and teaches how modern AI products are architected, scaled, and optimized in real world environments.

Through practical case studies, you will learn how technologies such as LLMs, RAG, AI Agents, Kafka, Redis, Kubernetes, Vector Databases, FastAPI, Databricks, and modern MLOps and LLMOps pipelines work together to power enterprise AI applications. You will also learn architecture patterns used in Machine Learning, Supervised Learning, Unsupervised Learning, Semi Supervised Learning, Natural Language Processing, and Computer Vision systems.

Whether you are transitioning into AI Engineering, preparing for AI System Design interviews, or building AI powered products, this course will help you think like a Senior AI Engineer and AI Architect.

What You Will Learn

Design end to end AI systems from requirements to architecture

Design scalable Machine Learning systems for production environments

Understand architecture patterns for Supervised Learning systems

Design Unsupervised Learning and clustering systems at scale

Learn how Semi Supervised Learning systems are deployed in production

Design Natural Language Processing applications using modern AI architectures

Understand Computer Vision system design and inference pipelines

Build scalable LLM and Generative AI applications

Design production ready RAG systems

Understand Vector Databases and Semantic SearchArchitect AI Agent and Multi Agent systems

Learn Kafka based event driven architectures

Design caching syst

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