Build real AI systems. Advance your career.
From curious to job-ready in 8 weeks.
A job-oriented program for working professionals and career changers who want to build with Generative AI — not just talk about it. Two evenings a week in Parsippany: prompting, RAG, and agents, ending with a deployed capstone and a GitHub portfolio you can put in front of hiring managers.
Who this program is for — and who it isn't
Eight focused weeks work best when expectations are honest on both sides. Read both columns before applying.
This is for you if…
- You're a developer, analyst, PM, or IT professional who wants hands-on GenAI skills — not another lecture series
- You're pivoting toward an AI-adjacent role and need a portfolio that proves you can build
- You can commit two weekday evenings plus a few hours of project work per week
- You're comfortable with basic programming, or willing to work through the Week 1 Python primer
- You want to ship AI features at your current job and speak credibly about how they work
This isn't for you if…
- You're looking for ML research training — model architecture math, training from scratch, or a path to a PhD
- You want a passive certificate with no project work
- You can't make time for the build work between sessions — the portfolio is the point
- You expect a job guarantee — we build proof of skill; hiring is still yours to win
What you leave with — and where it takes you
The program targets practical, in-demand roles where builders who understand GenAI systems are scarce:
- Deployed capstone — a working AI system running in the cloud with monitoring, demoed live on Demo Day
- Public GitHub portfolio — 6+ working projects, one committed every week of the program
- Resume & LinkedIn rewrite — positioned for AI roles, grounded in what you actually built
- Mock technical interview — practice defending your architecture choices out loud
The 8-week curriculum: every week ends with something working
Each week closes with a working artifact committed to your GitHub repo — by Demo Day the portfolio exists because you built it, week by week.
| Week | Module | You build |
|---|---|---|
| 1 | LLM Foundations & Prompt Engineering How LLMs work, context windows, system prompts, structured prompting — plus a Python quick-start for those who need it | A prompt-driven document analyzer |
| 2 | Working with AI APIs Claude/OpenAI APIs, structured outputs, function calling basics, cost & token management | A CLI assistant with tool calls |
| 3 | RAG Fundamentals Embeddings, chunking, vector databases, retrieval pipelines | A “chat with your documents” app |
| 4 | Advanced RAG & Evaluation Hybrid search, reranking, hallucination control, eval harnesses | An evaluated, production-pattern RAG service |
| 5 | Agents & Tool Use Agent loops, MCP, function calling in depth, memory patterns | A single agent that automates a real workflow |
| 6 | Multi-Agent Orchestration LangGraph, supervisor patterns, state management, human-in-the-loop | A multi-agent system with 3+ specialized agents |
| 7 | Production Readiness Guardrails, observability, security, cost optimization, cloud deployment | Your capstone deployed with monitoring |
| 8 | Capstone & Career Sprint Capstone demos, GitHub portfolio polish, AI-role resume/LinkedIn positioning, mock technical interview | Demo Day + interview-ready portfolio |
Week 1 — LLM Foundations & Prompt Engineering
How LLMs work, context windows, system prompts, structured prompting — plus a Python quick-start for those who need it.
You build: a prompt-driven document analyzerWeek 2 — Working with AI APIs
Claude/OpenAI APIs, structured outputs, function calling basics, cost & token management.
You build: a CLI assistant with tool callsWeek 3 — RAG Fundamentals
Embeddings, chunking, vector databases, retrieval pipelines.
You build: a “chat with your documents” appWeek 4 — Advanced RAG & Evaluation
Hybrid search, reranking, hallucination control, eval harnesses.
You build: an evaluated, production-pattern RAG serviceWeek 5 — Agents & Tool Use
Agent loops, MCP, function calling in depth, memory patterns.
You build: a single agent that automates a real workflowWeek 6 — Multi-Agent Orchestration
LangGraph, supervisor patterns, state management, human-in-the-loop.
You build: a multi-agent system with 3+ specialized agentsWeek 7 — Production Readiness
Guardrails, observability, security, cost optimization, cloud deployment.
You build: your capstone deployed with monitoringWeek 8 — Capstone & Career Sprint
Capstone demos, GitHub portfolio polish, AI-role resume/LinkedIn positioning, mock technical interview.
Demo Day + interview-ready portfolioBuilt for people with day jobs
Evening cadence
Tuesday & Thursday, 7–9 PM — two focused sessions a week that fit around a full-time role.
Saturday build-labs
Optional Saturday sessions (10 AM–12 PM) for pairing, debugging, and pushing your weekly project further.
Small cohort
Capped enrollment means your architecture questions get answered — and your code gets seen.
Code review
Weekly projects get instructor review — the habit that separates portfolio pieces from tutorial copies.
Learn from a practitioner, not a slide deck
Your instructor
The program is led by a practicing Enterprise AI Solutions Architect with 22+ years in enterprise technology — most recently at Verizon — with hands-on production experience in RAG and multi-agent systems, including LangGraph, MCP, and cloud AI platforms.
Cohorts & tuition
| Cohort | Dates | Schedule | Demo Day |
|---|---|---|---|
| Fall 2026 (Cohort 1) | Tue Sep 15 – Thu Nov 5, 2026 | Tue/Thu 7–9 PM + optional Sat build-lab | Sat Nov 7, 2026 |
| Winter 2027 (Cohort 2) | Tue Jan 12 – Thu Mar 4, 2027 | Same | Waitlist open |
The program meets in person at the Parsippany campus. Live-online availability is being finalized — note your preference in the application and we'll confirm options with you directly.
Frequently asked questions
Do I need to know how to code?
Comfort with basic programming is recommended but not required to start — Week 1 includes a Python quick-start primer. Analysts, PMs, and IT professionals who can read simple scripts do well here.
What's the weekly time commitment?
Plan for roughly 6–8 hours per week including class: two 2-hour evening sessions, the optional Saturday build-lab, and 2–3 hours of project work.
What tools and accounts do I need?
A laptop, a GitHub account, and API access to at least one frontier model provider. Setup is covered in Week 1, including cost management — typical API spend during the course is modest.
Is there a certificate?
Yes — a Y2 Academy certificate of completion. More useful in interviews: a public GitHub portfolio of working projects and a deployed capstone you can demo live.
What's the refund/withdrawal policy?
Refund and withdrawal terms are shared with your enrollment agreement before any payment. Contact the Parsippany campus for the current policy.
Is the program in person or online?
In person at the Parsippany campus. Whether live-online seats will be offered is being finalized — note your preference in the application and we'll confirm delivery options directly.
Apply for a seat
Tell us where you're starting from and what you're aiming for — we'll follow up within one business day with next steps and current tuition details. Cohorts are capped at 15.