AI Platform Engineering Manager

A course on AI system design interviews, from classic ML systems such as recommendations, ranking and fraud detection to LLM serving costs, RAG, generative AI products and agentic systems, with a repeatable framework and full case studies. It takes about 20 hours across 9 chapters. It suits engineers who can write code and know what a model, an API and a database are, and are facing design rounds that now include AI.

• AI
• System design
• Job search

A beginner course that gives software engineers and students a clear, practical grounding in modern AI: core concepts, training a simple model, neural networks and deep learning, search algorithms, reinforcement learning, agentic AI, modern tools and prompting, and the ethics and hype around it all. It takes about five hours across 14 chapters with playgrounds and assessments, and needs no prior AI background.

• AI
• Machine learning

The free, open companion to the Grokking the System Design Interview course, published as a GitHub repository. It teaches a pattern-first approach: learn building blocks such as caching, sharding, replication and queues well enough that any design question becomes a matter of combining them. It includes a seven-step interview framework, 30 patterns, 59 question walkthroughs, company guides, cheat sheets and study roadmaps, all readable in the browser without an account.

• System design
• Job search

An interactive practice tool for system design questions: you work through a real-world design step by step on a whiteboard and receive AI-powered feedback on your answer. Your practice sessions are saved with their status, so you can return to them and review results. It fills the gap most preparation leaves open, which is actually producing designs under realistic conditions rather than only reading worked answers.

• System design
• Job search

A six-stage path into paid remote AI data work, with a version for data annotation and one for AI trainers. It covers why models need people, the honest picture of pay, project gaps and scams, the guideline and quality skills platforms test, getting accepted, getting paid and keeping records, and moving up into reviewer, evals, data or ML roles. It suits students and graduates who want flexible remote income and read instructions carefully.

• Career
• AI
• Remote work
• Job search

A revision reference for paid AI data work, in 9 chapters and 64 to 66 concepts per edition: what labels, demonstrations, preference data and evals are, the honest picture of pay and contractor work, scam red flags, the guideline and quality skills tests check, annotation types and tools or the AI trainer's craft, getting accepted, a platform comparison, getting paid and moving up. It follows the Sefism AI data and training work roadmap chapter by chapter.

• Career
• AI
• Remote work
• Job search