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 LearnDesign end to end AI systems from requirements to architectureDesign scalable Machine Learning systems for production environmentsUnderstand architecture patterns for Supervised Learning systemsDesign Unsupervised Learning and clustering systems at scaleLearn how Semi Supervised Learning systems are deployed in productionDesign Natural Language Processing applications using modern AI architecturesUnderstand Computer Vision system design and inference pipelinesBuild scalable LLM and Generative AI applicationsDesign production ready RAG systemsUnderstand Vector Databases and Semantic SearchArchitect AI Agent and Multi Agent systemsLearn Kafka based event driven architecturesDesign caching syst