How to Get Into AI in New Zealand

How do I get into AI is the question I get asked more than any other. It usually turns up as a LinkedIn message from a student a year out from graduating, or from someone a couple of years into a job who can see where things are heading and wants in.

Most of the advice you find online is written for the United States, where the market is enormous and the job titles are specific. New Zealand is not that market. This is the version that actually applies here.

Start with what New Zealand businesses actually want

Most of this country runs on small and medium businesses. Almost none of them are training models. What they need is someone who can take tools that already exist, work out which process is worth changing, and make that change stick. A lot of it is not even AI, it is business automation with a bit of AI in the middle. You would not believe how many people still spend their day copying information from one system into another by hand.

That matters because it changes what you should learn. If you spend six months implementing neural networks from scratch, you will be extremely well prepared for a job that barely exists here. Research roles do exist, but there are few of them and they mostly sit inside universities and a handful of larger companies.

The roles that do exist look like the ones our members hold. In Breaking Into AI, Georgia Singleton described spending most of her time at Trade Me architecting solutions rather than writing code, and Brodie Dye described his role at Zuru as roughly half building and half translating technical work for marketing, commercial and product teams. Neither of those is a research job. Both are about applying tools well and communicating clearly.

You do not need a degree in AI

I studied mechatronic engineering at Massey. Nothing in that degree taught me AI. What it taught me was how to pull a problem apart, and that turns out to be the transferable bit.

Look at the people who have spoken at our meetups. Georgia switched out of computer science after an advisor told her she was not smart enough to work in AI, and she is now an AI engineer at Trade Me. Brodie tried life as a stockbroker first. Lachie Christie came through mechanical engineering and a design-thinking degree in Sydney. Caleb Wharton started in accounting. There is no standard entry path, largely because the field is too new to have grown one.

What all of them have in common is that they built things and they talked to people. That is the whole pattern. If you are waiting for a qualification to give you permission, you are waiting for something that is not coming.

Your first month: get genuinely fluent with the tools

Pick one assistant, Claude or ChatGPT or whatever you already have access to, and use it for real work every day for a month. Not for novelty. For your assignments, your job, the admin you keep putting off. You are building an instinct for what these models are good at, where they fall over, and how much context you need to give them before the output is worth having.

Then get on a coding tool. Claude Code and Cursor are the two most people in our community use. You do not need to be a strong programmer to start, which is exactly the point, but you do need to read what comes out and understand it. There is a real difference between someone who can prompt their way to working software and someone who ships whatever the model produced without checking. An interviewer can tell which one you are within about ten minutes.

Then learn one automation tool. n8n is the one I reach for most, and it is free to learn. Being able to wire a trigger to a few steps to an output will do more for your employability in the New Zealand market than another certificate will.

Do not try to learn all of it at once. Georgia's advice at our May session was to pick one thing and go deep, because most tools are the same thing in a different font and the skills transfer. She is right, and trying to keep up with everything is the fastest way to burn out without learning anything properly.

Your second month: build something small and real

The strongest thing you can put in front of an employer here is something you built that a real person uses. Not a tutorial you followed. Something with a user, even if that user is your mum.

My first build was a small web app for my partner that scanned a makeup product, read the ingredients and found cheaper alternatives. It had nothing to do with engineering. What it taught me was how to take someone's actual problem and turn it into a working thing, which is basically the entire job.

Three rules for a good first project. It has to solve a problem for a real person you can talk to, so you get feedback instead of guessing. It has to be small enough that you finish it, because an unfinished project proves nothing. And you have to be able to demo it in about sixty seconds, because that is roughly how long you get in an interview or standing at a meetup.

If you are stuck for ideas, look at the organisations around you. A family business, a sports club, a flat, a student association, the place you work part-time. Every one of them has someone doing a repetitive job by hand that they would love to stop doing.

Learn in public, because right now nobody knows you exist

Being good at the work is not enough on its own, and this is the part almost everyone skips. I started posting on LinkedIn and YouTube in May 2025. Within six months I had seven job offers across New Zealand and one in Australia, one of them at double my salary. All of it inbound, with no applications and no ad spend.

It does not work because I am good at making videos. The early ones are still up and they are genuinely bad. It works because almost nobody in New Zealand posts about the actual technical work they do day to day. The audience is small, but it is exactly the right people, and it costs nothing.

You do not need an audience or a content strategy. Post what you built this week and what broke. If you are a student, post what you are learning and what confused you. It feels cringe. It is only cringe until it works, and if a post lands you a job it was never cringe in the first place.

Talk to people, and make the ask small

In my first six months in a graduate role I cold messaged pretty much everyone on LinkedIn in New Zealand with AI in their title, and ended up speaking to around 75 of them. Roughly half replied. The ones who did not are not a rejection, they are just busy.

The point was never to ask for a job. It was to build a picture of the market that you cannot get any other way: who is doing what, what is genuinely working, and where the gaps are. My fractional role at Patersons came out of one coffee chat. My first paying client came from one YouTube video. Neither looked like much at the time, and that is the thing about these conversations, they compound.

Make the ask small and specific. Twenty minutes about what their team is actually building beats a paragraph about how passionate you are about AI. And turn up in person. Our meetups are free and run right across the country, and standing in a room with people who do this work is worth more than a month of scrolling. There is usually something on in Auckland, Wellington or Christchurch, and we run a monthly online catchup on Discord for everyone else.

Where the roles actually are

The hard part of finding an AI job here is that most of them are not advertised as AI jobs. They go out as graduate software engineer, data analyst, digital specialist or automation lead, with the AI work buried in the responsibilities. I have gone through where to look and how to approach it in Where to find AI internships and graduate roles in NZ.

Businesses keep asking me to find them AI talent and students keep asking me to find them AI roles, and I still cannot match the two up fast enough. The demand is real. The bottleneck is proof, not vacancies.

Do you need a masters?

It depends entirely on what you want to do. If you want to work in research, or you want to work overseas where the credential carries more weight, then it is worth it, and a good supervisor is worth a lot on its own.

If you want to build things for New Zealand businesses, think hard about it. A multi-year programme in a field that shifts every few months is a big bet, and the years of building and networking you give up are not free. That is a trade-off, not a rule, and plenty of people do both. Just be honest with yourself about which problem the qualification is actually solving. If you are weighing up study options, I have gone through them in AI courses in New Zealand: an honest guide.

The bit that actually matters

Nearly everything above is a variation on one idea: start before you feel ready. Every person I have hired, and every person I have watched get hired, did something before anyone asked them to. One of my contractors messaged me as a first-year civil engineering student saying his lecturers were not teaching any AI, and offered to work for free. Almost nobody offers that, so I got him upskilled and he ended up on a contract.

You are not behind. In any practical sense this field is only a few years old and nobody has it figured out, including the people who sound like they do. Pick a tool, build something small, tell someone about it, then do it again. If you would rather not do that alone, come and join us. It is free, and there is no catch. If you have got questions about how the community works, the FAQ covers most of them.