Careersy AI
AI Career Coach for AI Engineers

An AI career coach for AI engineers, and anyone going AI-native.

Generic AI gives you generic AI-career advice. Career-test apps are built for teenagers. Careersy AI is built for AI and ML engineers in Australia and New Zealand. And for people going AI-native from another role. By a recruiter who knows how these jobs get filled here.

Free to start. No card required

The gap

Everyone says they "do AI" now. The shortlist is short.

There are few real AI and ML jobs here. And a flood of people chasing them. To get picked you need a profile that shows the right work. A CV that proves you shipped models, not followed tutorials. And answers that hold up in a technical room. That is what it coaches.

1.0 · Get found

See how your profile reads to recruiters’ AI tools.

Recruiters use AI to find people now. AI Discoverability reads your profile the way their tools do, pulls live ads for your target role and checks your words against theirs. A status for each area, the lines to rewrite first, and a clear line between what it checked and what it cannot check from a profile.

Staff Engineer, platform

AI Discoverability

You are a well-evidenced platform engineer, and the thing holding you back is discovery: your headline leads with a stack rather than the words recruiters filter on. Once someone reads you, the evidence is convincing; the risk is that fewer of the right people ever open the page.

Where you stand· 5

We have not searched the web for your name. Graded from your CV, not from your live LinkedIn profile. 2 rows here cannot be judged from a profile alone, and each one tells you what it would need.

Found by recruiters and their AI tools

Your current title, Senior Backend Engineer, is what recruiters search, so that role enters the pool cleanly. The problem is the headline: it opens with the stack (Go, Kubernetes, Postgres) rather than the market’s phrasing for the level you want, and “platform” appears nowhere in the first line. Location and skills are solid, so this is a wording fix, not a scope problem.

Needs workKeyword thin

Read from your own words: Senior Backend Engineer | Go, Kubernetes, Postgres | Payments

Found by AI assistants such as ChatGPT, Gemini and Grok

Recruiters and AI sourcing tools do not look for you by name. They search for a role: a title, a location, the skills an ad asks for. Then they read whoever comes back. That is the search this report is built around. Someone typing your name into an assistant is a different question, and the one thing here not checked.

Cannot check from a profile

Proving fit

This is your real strength. The experience section binds team size, platform scale and outcomes to specific roles: 16 engineers across two squads, a platform serving 900+ internal users. These are bounded, role-anchored facts, exactly what a sourcing tool needs to rank you above someone who only lists skills.

StrongProven

Read from your own words: Scaled the platform team from 8 to 16 engineers while owning the roadmap for 900+ internal users.

Inside LinkedIn

This read is from your CV, not your live LinkedIn, so it cannot see whether Open to Work is on, whether you share a resume with recruiters, or when you last updated the profile. Those levers only move things inside LinkedIn Recruiter. Add your LinkedIn export in your profile and the next report reads the real thing.

Cannot check from a profile

Say it directly

You state Australian citizen, the one screening fact handled well, since work rights are a common early filter for Sydney roles. Not on the page: notice period, salary expectation, or what is motivating a move. No profile edit surfaces those. Say them to a recruiter or in your outreach.

Needs workProven

Read from your own words: Sydney, NSW | Australian citizen

Rewrites you can paste in· 2

Each one is built only from facts you already gave us. Copy it and paste it where the card says.

1Your LinkedIn headline

Staff-level Backend Engineer | Payments Platform, Distributed Systems | Go, Kubernetes, Postgres | Led 16 Engineers, 900+ User Platform

Copy your headline

2A bullet in your LinkedIn experience

Scaled the payments platform team from 8 to 16 engineers across two squads, owning the roadmap for a platform used by 900+ internal users.

Copy bullet 1

Inside LinkedIn Recruiter: settings a recruiter filters on

6 ⌄

What the ads ask for, checked against you

7 ⌄

Words these ads use that yours does not

8 ⌄

Lines a recruiter would quote

⌄

Live roles this read is measured against

12 ⌄

The smallest change that moves this

⌄

2.0 · Get seen

Beat the filter. Then win the human.

Your CV gets read three times. A filter scans for keywords. A recruiter skims for seven seconds. A manager decides if they want you. It scores your CV against the ad, names the words the ad asks for that your CV never says, and gives you the fix to make first.

ATS Score

ATS Score · 82 of 100

Strong match for this ad

Your CV scored 82 of 100 against this ad. Six keywords this ad asks for are not on your CV.

The coach’s read

How your CV reads against this one ad, in the report’s own words.

Strong match. The work the ad asks for is the work on file. The gap is the platform tooling the ad names and your CV never does, and that is the kind a scanner filters on before a person reads a line.

Missing keywords· 6

The ad’s own words that your CV does not use yet. Add the ones that are true of you, in the ad’s wording.

KubernetesTerraformon-callobservabilityGraphQLincident review

Kubernetes

Ran [what you ran] on Kubernetes across [how many services]. Add it only where it is true of you.

Score breakdown· 6

The score comes from these parts. The lowest row is where to start.

Keyword Match · 74 of 100

Six of the ad’s weighted terms are missing. Kubernetes and Terraform are the two a scanner weights most.

Skills Alignment · 85 of 100

Experience Relevance · 92 of 100

Education & Certifications · 78 of 100

Formatting & ATS Readability · 84 of 100

Recruiter Readability · 88 of 100

Fix this first· 4

Work down these in order. Each one is a change you can make to the CV today.

1Add Kubernetes and Terraform to the platform line, in the ad’s words

Your last role ran on both. The CV says “cloud infrastructure”, which a scanner does not count. Name them where they are true.

2Name the on-call rotation you ran

The ad lists it as a responsibility. Your incident work is on file; the phrase is not.

Scored against the ad, keyword by keyword.

3.0 · Get through the room

Practise the interview first.

It runs ML system design. The deep research questions. The coding rounds AI teams still set. Moving in? It helps you prove the real AI work you have already shipped.

Interview Preparation
Panel interview Thursday for a Staff role. What are they actually scoring?
  • 1 earlier step done
  • Thinking about your question...

Not your answers. Three things underneath them:

What the panel is scoring
JudgmentThe call you made when the brief was wrong, and why.
OwnershipWhat was yours to carry, and what you carried anyway.
LevelWhether you think in teams and quarters, not in tickets.
Build my stories
What the panel scores at that level, then your stories.

4.0 · Get paid properly

Ask for the right number.

It knows what your role pays here. Not in the US. It gives you the AU/NZ band for your level and city, shows where the job is bigger than the title, and writes the sentence you send back.

Compensation & Negotiation
The offer came through for a Senior role in Sydney. Is it fair, and how do I ask for more?
  • 1 earlier step done
  • Thinking about your question...
Where the offer sits
Median
75th
Your offer
Offer read
BaseBetween the median and the 75th for the title. Fair, not a ceiling.
TitleSenior, for Staff-level scope. It prices you as if the last three years did not happen.
The askBase first, it compounds. One number tied to your own outcomes, drafted below.
Draft the counter
The band for your level and city, then the sentence you send.

Four of the thirteen things it does, each one starting with you saying what is going on. Every mode, shown.

The difference

Three things call themselves a career coach.

One was built by a recruiter. The other two weren’t.

Generic AI(ChatGPT)Career-test appsCareersy AI
Built forEveryone. Every task.School leavers.AI and ML people in ANZ
Knows the ANZ marketNo. Defaults to the US.Sort of. Not tech.Yes. Local pay, employers, 482 visa.
Built by a recruiterNo. Guesses off the web.No. It is a quiz.Yes. 13 years hiring ANZ tech.
Makes things upYes. Then you defend it.N/ANever. It asks you instead.
Covers your whole careerOne prompt at a time.Stops at a job suggestion.Getting found, your next move, the hunt, the offer.
Who built it

We know who gets hired in tech. We spent years deciding it, from the hiring side of the table.

This is not a general AI with a careers prompt bolted on. It is the exact method Eli uses with coaching clients. The same recruiter’s eye behind 100+ people hired. The difference here is simple. You run it on every job. At any hour. For the price of one session.

It only works with what’s true. When a line is thin, it does not invent a number to fill the gap. It asks you for the real one, the way a recruiter would. Your CV stays yours. We never show it to employers. We never sell it. Because the fastest way to lose an offer is a claim you can’t back in the room.

Eli Gunduz

Built by Eli Gunduz, founder of Careersy Coaching. 13 years hiring in ANZ tech. 5,000+ interviews. 26,000+ resumes read.

Questions

Straight answers.

What is an AI career coach for AI engineers?

It is a coach for people building AI, and people moving into it. It separates real ML work from tutorial-following on your CV. It shows whether you are findable for the right jobs. It practises ML system design and technical interviews. Going AI-native from another role? It shows you how to prove the AI work you have already done. Built for the small, crowded ANZ AI market. By a recruiter who hires for it.

Is an AI career coach actually worth it?

Only if it knows your field. Generic advice is a waste of time. Here is the truth. Most good people do not miss out on skills. They miss out because recruiters cannot find them. Or their CV looks like everyone else. Or they undersell their work in the interview. A coach that fixes those three things is worth it. One that says "be confident" is not.

How is Careersy AI different from ChatGPT?

ChatGPT does everything. So it guesses at hiring. And it makes things up when it runs out of facts. Careersy AI does one thing. The ANZ tech job hunt. It knows what a recruiter screens for. It knows how your CV gets read. It knows what an interview really tests. And it asks you for the truth instead of inventing it.

Can an AI career coach replace a human career coach?

No. And it is not trying to. A human reads the room and keeps you honest. What this replaces is the cost and the wait. You get a recruiter’s read on your CV, your profile and your answers. At 11pm on a Sunday. On every job. For the price of one session. Same playbook. Just faster.

Does it work for the Australian and New Zealand tech market?

Yes. That is the point. It knows the local employers. The local pay. How recruiters here shortlist. And the visa rules that trip people up, including the 482. It is not a US product with Australia bolted on. The method comes from years hiring in ANZ tech.

How much does an AI career coach cost?

It starts at AUD $25 a month. Every mode included. A human tech coach here charges a few hundred dollars a session. This is the same recruiter method. You run it on every job. As often as you want. For a fraction of one session. See the pricing page for plans.

Careersy AI also coaches tech, software engineers and career changers.

See what a recruiter sees. Free.

Start a chat. See why recruiters in Australia and New Zealand keep missing you. And what to fix first.