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Keep shoppingAI System Design for Engineers and Interviews
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178 students
AI System Design for Engineers and Interviews
Design production ready AI systems through real world case studies on LLMs, AI Agents, Kafka, Kubernetes and ML at scale
Created byAritra Basak
Last updated 6/2026
English
What you'll learn
- Design production ready AI systems by translating business requirements into scalable architectures.
- Architect modern AI applications using LLMs, RAG, AI Agents, Vector Databases, Kafka, Redis, and Kubernetes.
- Analyze real world engineering trade-offs involving scalability, reliability, latency, throughput, and cost optimization.
- Design Machine Learning, Natural Language Processing, and Computer Vision systems used in production environments.
- Understand MLOps pipelines for model training, deployment, monitoring, and drift detection
- Evaluate and choose the right architecture patterns for AI platforms, inference systems, data pipelines, and distributed services.
- Break down complex AI products into scalable system components and communicate architecture decisions with confidence.
- Understand how leading technology companies design and scale AI systems serving thousands to millions of users.
- Approach AI System Design and Machine Learning System Design interview questions using a structured engineering framework.
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Course content
14 sections • 41 lectures • 8h 34m total length
Requirements
- Familiarity with software engineering fundamentals such as coding, APIs, databases, caching, and application deployment will help you get the most value from this course.
- A basic understanding of Artificial Intelligence and Machine Learning concepts, including LLMs, NLP, Computer Vision, or modern AI applications, is recommended.
- This course focuses on architecture and system design discussions rather than implementation, so learners should be comfortable understanding technical workflows, engineering trade-offs, and production system concepts.
Description
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 Search
Architect AI Agent and Multi Agent systems
Learn Kafka based event driven architectures
Design caching systems using Redis
Understand Kubernetes for AI workloads
Learn batch and real time inference architectures
Optimize AI systems for latency, reliability, and cost
Evaluate real world engineering trade offs
Approach AI System Design interviews with confidence
Real World AI System Design Case Studies
Google CTR Prediction System
HubSpot User Clustering System
Facebook Content Moderation System
AI Grammar Checker SaaS Platform
AI Interview Chatbot System
Smart Car Parking System using Computer Vision
Deep Research AI Agent
Autonomous Travel Booking Agent
Each case study focuses on architecture decisions, scalability challenges, infrastructure design, MLOps and LLMOps workflows, Machine Learning pipelines, Natural Language Processing systems, Computer Vision workloads, and production engineering trade-offs.
Why This Course Is Different
Most AI courses focus on:
Prompt Engineering
Framework Tutorials
Chatbot Projects
API Integrations
This course focuses on:
AI System Design
Production AI Architecture
MLOps and LLMOps
Scalability and Reliability
Distributed Systems
Infrastructure Design
Real World Engineering Thinking
You will learn how experienced engineers design AI systems that can scale beyond prototypes and demos.
Important Note
This is an architecture focused course.
This course does NOT include:
Coding projects
Model training exercises
Deployment labs
Instead, the focus is entirely on:
System Design
Architecture Diagrams
Engineering Trade Offs
Production AI Thinking
Who This Course Is For
Software Engineers transitioning into AI Engineering
AI and ML Engineers
Backend Engineers building AI products
Engineers preparing for AI System Design interviews
Technical Architects designing AI platforms
By The End Of This Course
You will be able to:
Design production ready AI systems
Architect scalable LLM, RAG, and AI Agent applications
Understand real world AI infrastructure, MLOps, and LLMOps ecosystems
Make better architecture decisions
Think like a Senior AI Engineer and AI Architect
If you want to move beyond AI tutorials and understand how real-world AI systems are architected and scaled, this course is for you.
Who this course is for:
- Software Engineers who want to transition into AI Engineering and learn how production AI systems are architected and scaled.
- AI Engineers and Machine Learning Engineers looking to strengthen their AI System Design and architecture skills beyond model development.
- Backend Engineers building AI powered applications using LLMs, RAG, AI Agents, Vector Databases, and modern AI infrastructure.
- Engineers preparing for AI System Design, Machine Learning System Design, or Senior Engineering interviews.
- Technical Architects and Engineering Leaders responsible for designing scalable AI platforms and enterprise AI solutions.
- Developers who already understand software development fundamentals and want to learn how real-world AI products are designed, optimized, and operated at scale.