This page documents the program structure, admissions standards, instructional methodology, curriculum, assessments, capstone requirement, faculty, learning outcomes, and the credential awarded. It is the authoritative educational profile of the program.
The AI Engineering Bootcamp is a one-year, full-time, cohort-based program delivered live online, structured as four 12-week terms.
| Program Name | AI Engineering Bootcamp |
|---|---|
| Institution | Artificial Intelligence Institute (artificialintelligenceinstitute.org) |
| Program Type | Full-time professional education program (bootcamp) |
| Delivery Format | Live online: synchronous lectures, mentored labs, and office hours |
| Duration | 48 weeks (one academic year), organized as four 12-week terms |
| Weekly Commitment | 40 hours per week (attendance required) |
| Total Instructional Hours | 1,920 contact hours, plus assigned self-study |
| Cohort Start Dates | September (Fall cohort) and March (Spring cohort) |
| Cohort Size | 24 students per cohort |
| Language of Instruction | English |
| Credential Awarded | Certificate of Completion with Official Transcript |
The academic year consists of four 12-week terms with a one-week break between terms. Two cohorts are admitted per year. The calendar below describes the Fall cohort; the Spring cohort follows the same structure beginning in March.
Software engineering, computer science fundamentals, data structures and algorithms, databases, and developer tooling. Weekly graded assignments begin in week 1.
Machine learning foundations, LLM APIs and prompting, embeddings and vector search, and RAG pipelines. Concludes with the midterm project and a practical coding assessment.
LangChain and LangGraph, agentic systems, cloud deployment on AWS, CI/CD for AI pipelines, and observability and evaluation. Team projects deploy to production.
Fine-tuning, security and guardrails, system design, and distributed systems, in parallel with the capstone project. Concludes with final assessments, the capstone defense, and graduation.
Admission is required and selective. The process evaluates programming readiness, mathematical preparation, and the applicant's capacity for a full-time, one-year course of study. Applications are reviewed on a rolling basis until each cohort of 24 is filled.
A written application documenting prior programming experience, educational background, and a statement of purpose. Faculty review every application individually.
A timed, practical coding assessment in Python covering control flow, functions, core data structures, and problem decomposition. A preparation guide with representative exercises is provided to all applicants.
A live technical interview with a member of the faculty, consisting of a pair-programming exercise and a structured discussion of goals, prior work, and readiness for the program's attendance requirements.
Instruction is full-time, cohort-based, and instructor-led. Each week combines live lectures, mentored labs, pair programming, team-based learning, and assigned self-study. Attendance is required and recorded as part of the academic record.
Daily instructor-led lectures introducing each module's learning objectives, followed by guided implementation sessions.
Afternoon lab blocks in which students apply lecture material to graded exercises with teaching assistants and industry mentors present.
Structured pairing rotations across the cohort, mirroring professional engineering practice and building code-review fluency.
Scheduled weekly office hours with instructors and teaching assistants for individual technical support and feedback review.
Recurring team projects with defined roles, sprint structure, and retrospectives, finishing with the team-built capstone.
Assigned readings, documentation study, and practice problems form part of the 40-hour weekly commitment and are reflected in weekly assessments.
The curriculum is organized into four terms of required coursework. Each module publishes learning objectives and is assessed through graded assignments and practical coding assessments. There are no electives; every graduate completes the full sequence.
Assessment is competency-based and continuous. Every assignment is graded, and grades are recorded on the official transcript. Students must maintain passing performance in each term to advance to the next.
Graded coursework submitted every week, combining AI-graded automated checks with human instructor feedback on code quality and design.
Timed, hands-on assessments at the midpoint and end of each term, evaluating applied competency rather than recall.
Automated evaluation harnesses measure the correctness, performance, and cost-efficiency of students' AI systems against published rubrics.
Structured human peer reviews of pull requests each week, graded for review quality and technical communication.
An individually built, end-to-end LLM application at the end of Term 2, assessed by faculty against the published rubric.
Term 4 concludes with comprehensive practical assessments and the capstone defense before a faculty review panel.
Graduation requires a documented portfolio of working software. Portfolio review is part of the final assessment, and every listed item below is required, not optional.
The capstone is an industry-scale production AI system, built by teams of three to four students over the final 12 weeks under faculty and industry-mentor supervision. Capstone completion is a graduation requirement.
A deployed, working system serving real usage, with authentication, observability, evaluation pipelines, guardrails, and documented operating costs.
A formal design document and architecture review with faculty at the project midpoint, mirroring industry engineering practice.
A recorded final demonstration and technical defense before a review panel of faculty and industry engineers, graded against a published rubric.
All instruction is delivered by practicing engineers. The teaching staff is led by a Program Director responsible for academic standards, the syllabus, and graduation requirements.
Senior software engineers, AI engineers, and staff engineers with production experience building and operating AI systems.
Program alumni and practicing engineers who staff mentored labs and office hours at a low student-to-staff ratio.
Engineering managers and specialists who deliver guest lectures and mentor capstone teams through architecture reviews.
6+ years of production experience building RAG pipelines, LangGraph agents, and enterprise AI systems. Instructs AIE 202, 204, 301, 302, 303, and 304. Full profile →
Support services run for the full academic year and continue through the first six months after graduation.
Graduates of the AI Engineering Bootcamp demonstrate competency in each of the following areas. Every outcome is assessed through graded coursework and evidenced in the graduate's portfolio and transcript.
Students who complete all required coursework, maintain attendance requirements, pass all practical assessments, and complete the capstone are awarded the program credential at graduation.
Employers and recruiters may verify any graduate's credential and request the official transcript by contacting join@artificialintelligenceinstitute.org.
Cohorts begin in September and March and are limited to 24 students. Contact the admissions office to begin your application or to request the full syllabus.
join@artificialintelligenceinstitute.org