AI Solutions Developer- UCI 3097

This 13-week course is designed to equip learners to build end-to-end AI applications—operating model endpoints, designing effective prompt strategies, implementing Retrieval-Augmented Generation (RAG), adding tool-using agents, integrating OCR-driven multimodality with a lightweight UI, and applying evaluation, testing, and documentation for production-minded delivery. This intermediate course is designed for individuals with prior software engineering training who are ready to use their skills in AI application development. Through an array of interactive labs, clear, focused mini-lectures, and collaborative build projects, individuals will stand up model runtimes, orchestrate conversations, ground answers in their own data, implement reliable tool-calling workflows, ship a demo UI, and assemble a portfolio-ready capstone.  Upon successful course completion, learners will be equipped to pursue roles such as AI Solutions Developer or Full-Stack Engineer.

Prerequisites

To be considered for this course, learners are required to meet the minimal qualifications for a Per Scholas course. It is expected that the learner will have technical knowledge through education, as a Per Scholas Alumnus, prior to entering the program. Additionally, learners must complete approximately 12 hours of self-study Tech Prep Work materials. Per Scholas will provide learner access to the materials and resources.

Topics include the following:

• Introduction to Python
• Intermediate Python
• Object-Oriented Programming in Python

Course Goals

Upon successful completion of all course requirements, learners will 

• Design end-to-end AI application architectures that compose prompting, RAG, agentic workflows, and multimodality.
• Implement model-orchestration flows with function/tool calling, memory, validation, and basic guardrails.
• Build retrieval pipelines (ingest, chunk, embed, index, retrieve) that ground answers with citations.
• Develop user-facing demos (CLI/API/UI) and package code with tests, configs, and reproducible environments.
• Evaluate performance, reliability, and cost, using logs, metrics, documentation, and stakeholder-ready presentations to report results.