Hire an AI engineer who has already shipped one.
Engage our people directly — an AI subject-matter expert, an AI developer, an AI consultant, a reliability or security engineer, or a forward-deployed engineer embedded in your team. Sydney-based, Australian hours, from one day a week. No recruiter margin, no twelve-week notice period, no learning on your project.
Eleven people. Tell us which one your problem needs.
Most companies ask for “an AI person” and mean one of eleven quite different jobs. If you're not sure which, we'll say so on the first call — including when the honest answer is that you don't need one yet.
AI Subject-Matter Expert
The senior person who has already shipped AI, on your side of the table.
- Sets the technical direction and picks the architecture
- Sizes cost, latency and risk before you commit to a build
- Reviews vendors, models and proposals you've been pitched
- Chairs the build and keeps it honest
Best when you're about to spend real money and want someone who has done it before to say whether the plan holds.
AI Developer / AI Engineer
The person who writes the thing and gets it into production.
- LLM, RAG and agent applications, end to end
- Model integration, prompt and context engineering
- APIs, integrations and the app around the model
- Evaluation harnesses, CI and production monitoring
Best when you know what you want built and need engineering capacity that already understands AI systems.
AI Consultant
The outward-facing one — translates between the business and the technology.
- AI readiness and data audits
- Use-case discovery, ranked by return and effort
- Costed roadmaps and build-vs-buy calls
- Explaining it to a board without hand-waving
Best when the organisation is arguing about where to start, and needs a ranked answer it can act on.
Forward-Deployed Engineer
Half engineer, half consultant — sits inside your team and ships from the inside.
- Works in your repo, your stack, your standups
- Finds the real problem by sitting next to the people who have it
- Ships a working fix in the same week it's identified
- Leaves your team able to run and extend it
Best when the requirement isn't written down yet — because it can't be, until someone is close enough to the work to see it.
Data / MLOps Engineer
The one who makes sure there's solid ground under the AI.
- Pipelines, ETL and warehouse modelling (dbt, Airflow)
- Data quality, lineage and access control
- Deployment, scaling and cost control on AWS, Azure or GCP
- Monitoring, alerting and the on-call runbook
Best when the AI isn't the hard part — the data underneath it is.
Responsible AI & Governance Lead
The person who keeps an AI system defensible to a regulator, a board or a customer.
- AI risk assessment and guardrail design
- Alignment to ISO 42001 and Australia's AI Safety Guardrails
- Privacy Act automated-decision readiness (10 Dec 2026)
- Audit trails, disclosure and appeal paths
Best when the decisions your AI touches carry consequence — money, entitlements, students, patients, employment.
AI Reliability Engineer
The one who can tell you whether the AI is getting better or worse — with a number, not a feeling.
- Evaluation suites and regression tests for output that is never identical twice
- Hallucination, drift and answer-quality monitoring in production
- Latency, token-cost and availability budgets, tracked release over release
- Fallbacks, graceful degradation and the runbook for when a model misbehaves
Best when the AI is already live and nobody can answer “is it better than last month?” — which is the point most AI projects quietly stop improving.
AI Security Engineer
The one who attacks your AI on purpose, so nobody else gets to first.
- Prompt-injection, jailbreak and data-exfiltration testing against your own build
- Agent permission boundaries, tool scoping and blast-radius limits
- OWASP LLM Top 10 review across the model, retrieval and tool layers
- Provenance review of the models, weights and components you depend on
Best when your AI can read internal data or take actions on its own — because from that moment a well-crafted sentence is an attack surface.
Applied AI / ML Engineer
The one who works on the model itself, not only the application wrapped around it.
- Fine-tuning, distillation and adapting open models to your domain
- Classical ML where it beats an LLM — forecasting, ranking, anomaly detection
- Benchmarking and model selection against your data, not a public leaderboard
- Embeddings, retrieval quality, and the measurement behind both
Best when a general model nearly works, and the last stretch of accuracy is where all the value actually sits.
AI Product Manager
The one who decides what gets built, and what “good enough to ship” means when the output is probabilistic.
- Turning a use case into a scoped, testable product definition
- Success metrics and acceptance criteria for features that can be wrong
- Prioritising against cost, risk and how much users will trust it
- Pricing, packaging and the business case for an AI feature
Best when engineering isn't your constraint — deciding what is actually worth building is.
AI Adoption & Enablement Lead
The one who makes sure the thing you paid for gets used after the launch email.
- Rollout planning, pilot groups and internal champions
- Role-specific training, not a generic “intro to AI” deck
- An acceptable-use policy people can actually follow
- Measuring real adoption and feeding what you learn back into the build
Best when the tool works and the usage numbers say otherwise — which is where most AI investment quietly dies.
From one day a week to a squad.
You don't need a defined project to start. Working out what's worth building is part of what the engagement is for. Every shape below is month to month — you can stop at the end of any of them.
Fractional AI lead
Senior AI direction without a six-figure permanent hire. Sets the strategy, reviews the work, and is on the hook for whether it lands.
Embedded FDE
One forward-deployed engineer inside your team for a quarter. The standard way to go from nothing to a working AI system your people rely on.
A small squad
An SME, an engineer or two and a data specialist against a defined outcome, with milestones. When one pair of hands isn't the shape of the problem.
Advisory on call
Someone to sanity-check an architecture, a vendor quote or a model choice before you commit. Low commitment, high leverage.
The hardest part of an AI project is working out what to build.
And that only becomes clear when an engineer is close enough to the work to see it. A forward-deployed engineer sits inside your organisation — your repo, your standups, next to the person who actually has the problem — and ships the fix in the same week they find it.
Discovery and delivery, by the same person
No requirements document handed over a wall and misread. The engineer who watched your team lose two hours to a manual process on Tuesday is the one who has it automated by Friday.
Inside your environment, on your terms
Your cloud account, your repo, your security controls, your access model. We work the way your engineering team already works rather than exporting your data into ours.
Your team learns by doing it with us
Pairing, code review and design discussion are the point, not a courtesy. The measure of a good engagement is that you need us less at the end of it than at the start.
Backed by the team behind them
One engineer on site, but architecture review, guardrail tooling, security practice and specialists available behind them. That is the part a contractor from a recruiter can't bring.
See how forward-deployed engineering fits our other services →
An Australian company, in Australian hours, under Australian law.
Offshore AI development is cheaper per hour and routinely more expensive per outcome — because the overlap is two hours a day, the data has to leave the country, and nobody on the other end has read the Privacy Act.
In the room, not on a night-shift call
Sydney office in Surry Hills. We work on site across greater Sydney and in Australian business hours — the whole working day overlaps with your team, not two hours of it.
An Australian company, on Australian paper
Evolve Mind Solutions Pty Ltd, ABN 41 672 546 217. You contract with an Australian entity under Australian law, and you can look us up on the ABR before you call.
Your data stays onshore
We deploy into Australian regions on AWS, Azure and GCP — data residency you can point to in a procurement questionnaire, not a promise in an email.
We already know the rules you're bound by
The Privacy Act automated-decision rules landing 10 December 2026, the Australian Privacy Principles, ISO 42001 and the Government's 10 Voluntary AI Safety Guardrails. Not a research task for us.
Sydney on site. The rest of Australia in your time zone.
For clients outside Sydney we travel for the parts that genuinely need to be in person — discovery workshops, go-live, handover — and work remotely in between.
Sydney NSW
On site & remote
Melbourne VIC
Remote & travel
Brisbane QLD
Remote & travel
Canberra ACT
Remote & travel
Perth WA
Remote
Adelaide SA
Remote
Hobart TAS
Remote
Darwin NT
Remote
What people ask before they engage someone.
Can I hire an AI developer or AI consultant in Sydney?
Yes. Evolve Mind Solutions is a Sydney-based Australian AI company and you can engage our people directly — an AI subject-matter expert, an AI developer, an AI consultant, a forward-deployed engineer, a data/MLOps engineer, a responsible-AI and governance lead, an AI reliability engineer, an AI security engineer, an applied AI/ML engineer, an AI product manager or an AI adoption and enablement lead. Engagements run from one day a week to a full embedded squad, on site across greater Sydney or remote anywhere in Australia.
What is a forward-deployed engineer (FDE)?
A forward-deployed engineer is an engineer who works inside your organisation rather than at arm's length — in your codebase, your standups and next to the people who have the problem. The model exists because the hardest part of an AI project is usually working out what to build, and that only becomes clear when an engineer is close enough to the work to see it. An FDE does the discovery and ships the fix, often in the same week.
What is an AI reliability engineer?
An AI reliability engineer is the SRE of an AI system: the person who makes a non-deterministic product behave predictably enough to depend on. They build the evaluation suites and regression tests that catch quality dropping between releases, monitor for hallucination and drift in production, hold the system to latency, cost and availability budgets, and own the fallbacks and incident runbook for when a model misbehaves. The role exists because a traditional uptime check cannot tell you an AI is up and confidently wrong. It is usually the first hire a company makes after its first AI feature goes live.
What does an AI security engineer or AI red teamer do?
An AI security engineer tests an AI system the way an attacker would, with your authorisation and against your own build: prompt injection, jailbreaks, attempts to make the system leak internal data, and abuse of any tools or actions an agent can reach. They then tighten the permission boundaries, scope the tools and limit the blast radius, working from the OWASP LLM Top 10 across the model, retrieval and tool layers. It is a different job from AI governance: governance asks whether a decision is defensible to a regulator, security asks whether the system can be made to misbehave in the first place. Any AI that can read internal data or take actions needs both.
What's the difference between an AI consultant, an AI engineer and an AI SME?
An AI consultant works out where AI pays off and builds the roadmap. An AI engineer (or AI developer) writes the software and puts it into production. An AI subject-matter expert is the senior technical authority — the person who sets the architecture, sizes the risk and says whether a plan will hold before you spend on it. Small organisations often need one person covering all three; larger ones split them. We'll tell you honestly which one you actually need.
Can we hire one engineer, or do we have to buy a whole project?
One person is a completely normal engagement. The most common starting shape is a single forward-deployed engineer two to three days a week for a quarter, or a fractional AI lead one day a week. You do not need a defined project to begin — working out what's worth building is part of what the engagement is for.
How quickly can you have someone working with us?
Usually within one to two weeks of agreeing scope, and we'll normally do a free readiness conversation first so the person arrives already understanding your systems. If it's urgent, say so — we'll tell you straight away whether we can staff it rather than stringing you along.
How is this different from hiring an AI contractor through a recruiter?
You are engaging a company that ships AI into production, not a résumé from a database. Our engineers arrive backed by the team behind them — architecture review, guardrail tooling, security practice and the ability to add specialists mid-engagement. There is no recruiter margin, no three-month notice period, and if the fit is wrong it is our problem to fix, not yours.
Do your engineers work on site in our office?
Yes, on site across greater Sydney, and hybrid or fully remote anywhere in Australia. For Melbourne, Brisbane and Canberra engagements we travel for the parts that genuinely need to be in person — discovery workshops, go-live, handover — and work remotely in between, in your time zone.
What does it cost to hire an AI engineer or consultant in Australia?
We quote a day rate or a fixed monthly fee against an agreed shape — fractional lead, embedded engineer, or a small squad — after a free scoping conversation. It is deliberately smaller than a big-four consulting engagement and structured so you can stop at the end of any month. We won't quote a number before we understand the work, because a number without scope is meaningless.
Are your engineers based in Australia?
Yes. The engagement is run out of our Surry Hills office in Sydney, in Australian business hours, by an Australian-registered company (ABN 41 672 546 217). Your work is not handed to an offshore delivery centre overnight.
Can you help us hire and build our own internal AI team?
Yes, and we'd rather you did. We're not a recruiter, but knowledge transfer is built into every engagement — your team works alongside ours and keeps the code, the architecture and the reasoning. Where clients are standing up an internal AI function, we help scope the roles, sit in on technical interviews and mentor the people they hire.
More answers on the full FAQ →
Are you an engineer looking to join us rather than hire us? Register your interest →
Tell us the problem. We'll tell you who you need.
A free scoping conversation: what you're trying to do, which of the eleven roles actually fits, and what a sensible first engagement looks like. If the answer is that you don't need to hire anyone yet, we'll say that too.