Role path
AI Engineer
Build LLM features and agents, measure them with evals and run them in production.
8 tools11 courses5 lines67 lessons open4 career stages41 h in total
Tools this role uses
The labs and templates for real work that the ai engineer route opens. Use them on the job today; the courses below teach why they work.
Fresher
Station 1How the Web WorksFollow one request from a phone to a server and back, design APIs that survive real networks, and put numbers on speed, load and reliability.Change to the Developer Foundations line
Station 2JavaScript Deep DiveLearn how JavaScript really behaves, from values and closures to the event loop and async code, by fixing real bugs in a grocery chain's apps.Change to the AI Engineering line
Station 3Vibe Coding: Build Apps with AIBuild and ship a real app by describing it to AI coding tools, and learn enough to review, fix and secure what they write.Junior
Station 4LLM Apps: Prompts, RAG and EvalsBuild reliable features on large language models: prompts, structured output, tool calling, retrieval (RAG) and evaluation.- Station 5AI Systems for BuildersLearn how AI features really work, how the biggest models are served, and how to build and code with AI safely.
Change to the Developer Foundations line
Station 6Building APIs with Node.jsDesign and build a production-ready REST API with Node.js, TypeScript, NestJS and PostgreSQL.Mid-level
Change to the AI Engineering line
Station 7Building AI AgentsDesign, build and evaluate AI agents that use tools and real data safely, from a first prototype to production.Change to the Cybersecurity line
Station 8Application Security for DevelopersBuild software that resists attack: the OWASP Top 10, threat modelling, secure code review, supply chain and AI app risks.Senior
Change to the AI Engineering line
Station 9AI Engineering in ProductionRun AI features and agents at scale: observability, guardrails, model routing, fine-tuning choices, red-teaming and governance.Change to the Data line
Station 10Machine Learning in ProductionTake models from a notebook to a reliable service: pipelines, deployment, monitoring, drift and retraining (MLOps).Change to the System Design line
Station 11Scaling Building BlocksLearn the building blocks that let an app grow from one server to millions of users: load balancers, caches, object storage and CDNs, queues, rate limits and real-time updates.