The AI Engineering Bootcamp is a 48-week, instructor-led, cohort-based program: 1,920 instructional hours in software engineering, machine learning, LLM systems, and production deployment, with an industry-scale capstone and a formal credential.
The Artificial Intelligence Institute trains software engineers to design, build, and operate production AI systems. This is a structured educational program, not a short-term training course.
The Institute runs one program: the AI Engineering Bootcamp, a one-year, full-time program of study. Admissions, live instruction, graded coursework, mentored labs, portfolio projects, and the capstone requirement are all documented publicly so employers and recruiters can evaluate the program.
Instruction is cohort-based and led by senior software engineers, AI engineers, and staff engineers with production experience — including Christiam Ipanaque, an AI Engineer with six years of production experience building RAG pipelines, LangGraph agent systems, and enterprise AI deployments. Students are assessed against published learning objectives through practical coding assessments, technical evaluations, instructor feedback, and peer review. Graduates receive a Certificate of Completion, an official transcript, and a portfolio of deployed, production-grade AI systems.
Read the Full Program DetailsFour 12-week terms, 40 hours per week. Each term builds on the one before: CS fundamentals in Term 1, then LLMs, then production systems, then a team-built capstone.
Python, computer science fundamentals, data structures and algorithms, databases, APIs, Linux, Git, and backend development. The software engineering base every AI engineer requires.
Term 1 curriculum →LLM APIs and prompting, structured outputs, text embeddings, vector search with Qdrant, and RAG pipelines - starting with basic RAG, moving to HyDE re-ranking and multi-modal retrieval.
Term 2 curriculum →LangChain and LangGraph, tool-using autonomous agents, multi-agent orchestration, AWS deployment (ECS / Lambda), CI/CD for AI pipelines, and LLM observability with LangSmith.
Term 3 curriculum →Fine-tuning with LoRA, security and guardrails, on-premise LLM deployment, distributed systems. The term finishes with a team-built, industry-scale production AI system and technical defense.
Capstone requirements →Assessment in the program is competency-based and portfolio-based. Graduates leave with graded coursework, deployed systems, and a completed capstone as evidence of each outcome.
Design, build, and operate end-to-end AI applications: LLM integration, RAG pipelines, agentic systems, semantic caching, rate limiting, automated evaluation, all deployed to cloud infrastructure.
All learning outcomes →Every student graduates with individual, team, and capstone projects in production, along with a public GitHub portfolio recruiters can inspect, including open-source contributions and architecture documentation.
Project requirements →Graduates receive a Certificate of Completion, an official transcript of graded coursework, and a shareable digital credential. One authoritative record for résumés and LinkedIn.
Credential details →Admission is required for enrollment. The process evaluates programming readiness and commitment to a full-time, one-year course of study. Cohorts begin twice per year, in September and March.
A written application covering prior programming experience, mathematics background (algebra and introductory statistics required), and a statement of purpose. Applications are reviewed on a rolling basis.
Prerequisites →A timed, practical coding assessment in Python evaluating fundamentals: control flow, data structures, and problem decomposition. A preparation guide is provided to all applicants.
Assessment format →A live technical interview with a member of the faculty: a pair-programming exercise and a discussion of goals, prior work, and readiness for 40 hours per week of instruction and study.
Interview details →Prospective students, employers, and recruiters may contact the admissions office for the full syllabus, academic calendar, and verification of graduate credentials.
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