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tomoro-ai · Melbourne, Victoria, Australia · posted today
As an AI Adoption Specialist, your job is to help enterprise teams work better with AI by changing how work gets done.
You’ll sit at the intersection of workflow design, AI literacy, and the effective use of state-of-the-art general purpose AI tools. Some weeks you're embedded with a client team, observing how work really happens and redesigning it so AI carries the load. Other weeks you're creating the assets and approaches that make the new way of working stick: prompts, skills, quality checks, lightweight automations/ integrations, and simple tools built collaboratively alongside client teams.
You'll work alongside Tomoro's AI Engineers. They build production-grade AI systems. You make sure the humans around those systems (and the humans who don't yet have those systems) are getting the most out of AI, every day.
Think of the goal as five days of outcomes in three days of effort.
Requirements
You don't need to come from one specific background. We're looking for people who combine a few things that rarely sit together.
You might be a freelance AI practitioner who's been helping companies adopt AI tools and wants to do it at scale. You might come from an ops or enablement role inside a product company, where you've seen first-hand how hard it is to change how people work. You might have an L&D or behavioural change background and have been pulling AI into your practice because it's clearly where the value is. Or you might be a domain expert - someone deep in a specific field who got frustrated with inefficient ways of working and taught yourself to use AI to do your job faster and better, and now you want to help others do the same.
What matters more than your title is how you work.
These things are the most important attributes for you to be successful.
You default to AI. Not because it’s novel, but because it helps you get things done. You iterate fast, verify intelligently, and use AI throughout your everyday work.
What good looks like:
You care about whether something is useful and whether it will actually be adopted.
What good looks like:
You can help people build capability without turning it into training theatre.
What good looks like:
You love exploring AI tools and their application. You know what's available and how to apply it to real work constraints.
What good looks like:
You work things out. You don't wait for permission or perfect information.
What good looks like:
Benefits
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Curated listing sourced from the employer's public careers page.