Build an Interview Ready AI Portfolio

Enroll

Everyone applying for your roles lists the same skills, and hears the same nothing back. What separates you now is a working portfolio, meaning real AI projects a hiring manager can click, run and question. This course is build-first, so you leave with three finished projects rather than another certificate you cannot demonstrate. If you have never been an engineer, that is fine. You will build with Claude Code, Codex and Cursor, and you do not need to write production code from scratch in order to have something real to show. And if AI is the reason a role of yours disappeared, this is the fastest route to making it the reason you get the next one.

The outcome

How does this prepare you for a job?

Interviews do not test recall. You are handed a problem and watched to see whether you choose the right tool for it. Skill demonstrates itself, and a portfolio you actually built outweighs a rehearsed interview script.

Built and deployed a retrieval-augmented generation assistant, meaning RAG, over a private document corpus using Claude and pgvector, covering chunking, embedding and evaluation of retrieval.
Shipped an agentic workflow with tool-calling and human-in-the-loop review gates, which automates a multi-step research-and-drafting task.
Designed an evaluation harness combining a golden set with LLM-as-judge, to measure answer accuracy and catch hallucination regressions before release.
Ran user interviews, translated the findings into product requirements, and shipped the prototype that resulted.
Documented the trade-offs in model selection, token cost and latency across frontier and open-weight models.
Deployed projects to Vercel with CI, and instrumented usage analytics.

Tools

AI tools learnt

You work in the tools hiring managers actually name, rather than the automation builders that do not transfer.

Hands-on skills

Reverse-engineer a job description into the three or four signals a hiring loop actually screens for, then choose projects that prove them
Chunk, embed and index a private corpus, then tune retrieval through top-k and re-ranking until the answers stop being wrong
Write an eval set and an LLM-as-judge rubric, then measure accuracy, groundedness and hallucination rate across model versions
Design an agentic workflow covering tool-calling, retries, guardrails, and the human-in-the-loop gate that stops a bad action
Budget tokens, latency and cost, and choose between frontier and open-weight models using numbers rather than impressions
Instrument the product with analytics and read the funnel, then interview five users and turn the transcripts into a prioritized backlog
Package a build into a thirty-second demo, a live URL, and an interview narrative that survives follow-up questions

Syllabus

Modules

Level: Basic

Read the market before you build

Reverse-engineer a target job description into the signals a hiring loop screens for
Map each signal to an artifact you can demonstrate
Choose the first project, and cut it down to a slice you can demo in two weeks

Your first shipped AI project

Stand up a RAG assistant over your own documents with Claude Code and Cursor
Chunk, embed and index, and get retrieval right before you touch the UI
Deploy to Vercel, so the project has a live URL from the first day

Make it defensible

Write the thirty-second blurb covering what it is, why it exists and what it changed
Record a two-minute demo a hiring manager can watch without you present
Rehearse the follow-up questions an interviewer will ask about your choices

Level: Advanced

Build to the FAANG bar

Add an evaluation harness combining a golden set with LLM-as-judge, which is the artifact that separates a demo from production
Set explicit cost and latency budgets, the way a FAANG launch review requires
Handle the failure modes reviewers probe: hallucination, prompt injection and stale retrieval

Agentic systems with guardrails

Build a multi-step agent with tool-calling and human-in-the-loop approval
Add scoped permissions and audit trails, which is the enterprise gate every big-company deployment hits
Debug drift, meaning why an agent's output degrades over a long run and how you catch it

Case studies FAANG teams actually argue about

Netflix, and the trade-off between ads and engagement: model the objective and defend the metric you would optimize
Ranking and recommendation at scale, weighing relevance against latency against cost
Turn one case into a written design doc and defend it live, the way a FAANG system-design loop runs