Master GenAI engineering disciplines.
12 disciplines mapped to the real-world GenAI job descriptions that teams hire for. Each bundles the reading material, quizzes, labs, and open-source PR analysis to take an engineer from capable to production-ready.
All 12 disciplines
GenAI Application Engineering
Build production RAG & prompt chain applications, design streaming chat UIs, implement guardrails & evaluation, optimize LLM inference costs, and deploy on Kubernetes.
GenAI Agent Engineering
Build autonomous multi-agent systems with planning, reasoning, tool use, memory, MCP/A2A protocols, safety boundaries, and production evaluation.
GenAI Solutions Architecture
Design enterprise GenAI reference architectures, create ADRs and technical standards, bridge GenAI with enterprise workflows.
GenAI Data Engineering
Build RAG data pipelines for ingestion, chunking, embedding, and indexing. Manage vector store operations and embedding model lifecycle.
GenAI Inference Engineering
Architect multi-provider LLM gateways, implement semantic caching and batch optimization, monitor provider SLAs, and optimize inference costs.
GenAI Platform Engineering
Build internal GenAI developer platforms with self-service capabilities, multi-tenancy, RBAC, CI/CD for model/prompt/guardrail pipelines.
LLMOps Engineering
Monitor hallucination rates and token costs, operate guardrails and eval gates, manage prompt versioning and canary deployments.
GenAI Security Engineering
Engineer defenses against prompt injection, jailbreaks, and data exfiltration. Implement PII leakage detection, content safety, and compliance.
Forward Deployed GenAI Engineering
Rapid-prototype GenAI solutions on customer infrastructure, integrate GenAI with customer data and workflows, scope solutions with delivery methodology.
GenAI Safety & Evaluation Engineering
Design automated LLM evaluation pipelines, red-team GenAI systems, build bias detection and fairness benchmarks, implement guardrails.