AI Engineering

AI engineering is the discipline of integrating AI into a product and keeping it running: scoping product requirements into AI problems, picking models and tools, building retrieval and agents, testing and evaluating the results, and deploying and monitoring what ships. The AISL material covers exactly this arc, and this page is the entry point into all of it.

The role

The role definition comes from two articles that approach it from opposite directions. What Is an AI Engineer? An Experience-Based Definition defines the AI engineer as the person responsible for integrating AI into the product — making sure the AI part runs reliably, can be evaluated, and can be maintained over time. What Is an AI Engineer? Based on 1,000+ Job Descriptions checks that definition against the market: 889 unique listings from five tech hubs, parsed with an LLM pipeline into skills, tools, and responsibilities.

The two agree on the core: this is a production role, not a research role. The engineer owns prompts and their versioning, success metrics and test suites, deployment, monitoring, and cost.

The job-description research has since scaled into the AI Engineering Field Guide: 6,964 postings across eight monthly scrapes (Feb-Aug 2026), tracking role archetypes, skills demand, and trends — evaluation skills are the differentiator, explicitly required by 59.7% of AI-First roles while RAG and agents are baseline. The wiki keeps its sections as records: the role, the generated market tables, and the per-page market-wiki cards — skills, use cases, industries, companies and locations, and month-over-month trends.

The process

CRISP-DM for AI Engineering maps the 1996 cross-industry data mining process onto modern AI work: business understanding before modeling, data preparation before prompts, evaluation before deployment, and iteration through all of it. If the role definition says what an AI engineer is, this is the how.

The skills

The AI Engineer Learning Path turns the job-description research into a visual path: learning stages, skills by category, tools and frameworks, and portfolio projects. The rest of the material fills in the path:

• Python first: Python for AI Engineering
• The working fundamentals: LLM APIs, Prompt Engineering, and Structured Output
• Retrieval: RAG and Vector Search
• Agents: Agents and Building Coding Agents
• Production: Agent Deployment, Guardrails, Evaluation and Monitoring

The field guide's learning paths recut this same path per starting role — backend, data engineer, data scientist, frontend, ML engineer — from the job-market data and the buildcamp curriculum.

Where to go next

• Plan your route on the AI Engineering Roadmap
• If you are job hunting, start at AI Engineering Career
• If you want one course that covers the whole arc, see the AI Engineering Buildcamp