We run one program: the AI Engineering Bootcamp — 48 weeks, full time, 1,920 instructional hours across four 12-week terms, taught live by practicing engineers. Students move from software engineering fundamentals through applied LLM engineering and agentic systems to production deployment, and finish with a team-built capstone defended before a review panel of faculty and industry engineers. Graduates hold a graded transcript, a verifiable credential, and a portfolio of deployed, operating systems.
The program's full educational profile is published at artificialintelligenceinstitute.org. This GitHub organization is the Institute's documentation registry: the versioned, inspectable specification behind the credential — why we teach what we teach, how we assess it, and the open standards work we are building on top of that practice.
This repository (artificial-intelligence-institute-org/.github) contains the authoritative, governed record of the Institute's charter, program specification, curriculum, assessment methodology, and standards infrastructure. Every document is versioned and maintained by a designated office; the full index is the root README.md.
| Area | Start here | What you will find |
|---|---|---|
| Who we are | institute/charter.md |
Mission, one-program principle, the public-documentation commitment |
| Program specification | program/ |
Structural parameters, admissions, instructional model, assessment methodology, portfolio and capstone requirements, learning outcomes |
| Curriculum | program/curriculum/ |
All 16 modules (AIE 101–495), term by term, with objectives and assessment |
| Employers | employers/evaluating-a-graduate.md |
How to read a transcript, inspect a portfolio, and verify a credential |
The Institute's charter (§5) commits to building open, program-independent standards infrastructure alongside its teaching program. Both initiatives below are in development — the design frameworks are published, the instruments are being piloted internally, and nothing in either initiative is a live certification or a completed standard.
An objective, scenario-based evaluation standard for AI engineering competency — measuring the quality of engineering decisions under realistic conditions, including a scored professional-judgment dimension. Not a quiz, not a knowledge test. The design is inspired in spirit by how rigorous universities structure defensible, criterion-referenced examination and how leading AI labs structure deep, judgment-based hiring bars — and adds what neither does: a fully public, versioned, auditable method.
The standard's design record, taxonomy, scenario format, scoring model, and roadmap are published in competency-standard/.
The machinery that operationalizes the standard: rubric authoring and format specs, grader calibration protocol, inter-rater reliability targets, scenario authoring guide, and versioning/provenance discipline. Published openly so any employer, educator, or engineer can audit how a judgment was reached — and so other institutions can adopt or critique the framework.
The infrastructure specifications and a fully worked illustrative rubric are in infrastructure/.
- Program details and admissions: join@artificialintelligenceinstitute.org
- Website: artificialintelligenceinstitute.org
- LinkedIn: linkedin.com/company/artificialintelligenceinstitute
- Employer credential verification: AII-EMP-003