New opportunity

Senior AI Product Builder

Macquarie Group · Sydney, Australia

About this role

The Senior AI Product Builder joins Macquarie’s Enterprise AI Enablement team in Sydney to develop practical AI-powered solutions for business teams. This is a permanent, full-time role focused on identifying operational challenges and delivering solutions that improve ways of working and produce measurable business outcomes.

Responsibilities

  • Work with business stakeholders to understand needs and design AI-powered solutions.
  • Build, configure and deploy AI agents, skills and automation solutions using low-code and no-code tools. Own delivery from initial concept through adoption and ongoing value delivery.
  • Collaborate with business and technology teams to align on solutions, support organisational change and promote responsible AI practices.
  • Mentor colleagues, influence stakeholders and help shape the organisation’s evolving AI capabilities.

Requirements

  • 7–10 years’ experience in product management, business analysis, applied technology, AI enablement or related roles in fast-paced delivery environments.
  • Experience building, configuring and deploying AI-powered solutions, agents, automation tools or digital products with low-code and no-code platforms.
  • Strong analytical and problem-solving skills, including translating complex business challenges into practical, measurable solutions.
  • Strong communication and stakeholder engagement skills, with experience supporting adoption, user engagement, testing and change initiatives. An understanding of governance and risk requirements in a regulated environment is required.

Macquarie lists benefits that may depend on employment type, including wellbeing and other leave, learning and development opportunities, and salary packaging. Hybrid and flexible working arrangements depend on the role.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI agentsLow-code platformsNo-code platformsAutomationProduct managementBusiness analysisStakeholder engagementChange managementSolution testingAI governanceRisk managementProblem-solving

Your skill match

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Turn operational needs into measurable solutions

    The role works with business stakeholders to understand operational challenges and design practical AI solutions. The posting also calls for translating complex problems into measurable outcomes.

  • Own delivery beyond deployment

    The role builds, configures and deploys AI agents, skills and automations with low-code and no-code tools, taking ownership from concept through adoption and ongoing value delivery.

  • Align business and technology teams

    The posting emphasizes collaboration and influence across both groups to agree on solutions, support change and drive outcomes.

  • Enable responsible use and wider capability

    The role promotes responsible AI practices, requires understanding of governance and risk in a regulated environment, and includes mentoring colleagues and shaping evolving AI capabilities.

Likely challenges to think through

  • Balancing speed with governance

    Preparation hypothesis: fast delivery of low-code AI solutions may need to be balanced against risk and governance requirements. Prepare an example of how you would identify risks, involve the right stakeholders and test a solution before wider use.

  • Proving value after launch

    Preparation hypothesis: deploying an agent may be easier than securing adoption and showing sustained improvement. Prepare to explain how you would set an outcome measure, gather user feedback and refine a solution after release.

  • Reconciling different stakeholder needs

    Preparation hypothesis: business teams may prioritize operational usefulness while technology teams focus on implementation constraints. Prepare an example of how you would clarify the trade-off, reach alignment and keep delivery moving.

03

Practice your answers

Technical practice

How would you identify an operational challenge worth addressing with AI?

What to coverSuggested answer focus: Explain how you would map the current workflow with business stakeholders, identify the underlying problem and establish a baseline. Discuss when a simpler process change or conventional automation would be more appropriate than AI.

How would you turn a business need into a measurable AI solution?

What to coverSuggested answer focus: Describe how you would define users, scope, expected outcomes and success measures before building. Distinguish whether the solution works technically from whether it improves the way the team works.

How would you design an AI agent, skill or automation for a business workflow?

What to coverSuggested answer focus: Walk through the task boundaries, inputs, outputs and points where a person should review the result. Explain how you would choose between an agent, a narrower AI capability and straightforward automation.

What trade-offs would guide your choice of a low-code or no-code approach?

What to coverSuggested answer focus: Compare speed of delivery with the control, maintainability and integration needs of the solution. Explain how you would check that the approach remains suitable as usage or requirements change.

How would you take an AI solution from concept to deployment?

What to coverSuggested answer focus: Outline a practical sequence for discovery, configuration, testing, release and ownership after launch. Address how you would coordinate decisions with business and technology teams rather than treating deployment as the end of delivery.

How would you test an AI-powered solution before wider use?

What to coverSuggested answer focus: Describe tests based on representative user tasks, expected outputs, failure cases and stakeholder feedback. Explain how you would judge both solution quality and whether the workflow is usable.

How would you troubleshoot an AI automation that produces inconsistent results?

What to coverSuggested answer focus: Explain how you would reproduce the issue and isolate whether it comes from inputs, configuration, workflow steps or expectations about the output. Describe how you would verify a fix and decide when human review is needed.

How would you account for governance and risk when designing an AI solution?

What to coverSuggested answer focus: Discuss how you would identify relevant data, access and output risks, then involve the appropriate stakeholders before deployment. The posting calls for responsible AI in a regulated environment but does not name a specific control framework.

How would you evaluate value after an AI solution is adopted?

What to coverSuggested answer focus: Compare outcomes with the original operational baseline and gather evidence about usage and user experience. Explain how those findings would guide changes to the solution or a decision not to expand it.

Behavioural practice

Tell me about a time you influenced stakeholders with different priorities.

What to coverSuggested answer focus: Use a real situation to explain the competing priorities, the action you took to build alignment and the result. Make your own contribution clear without implying that agreement came easily.

Tell me about a time you helped people adopt a new way of working.

What to coverSuggested answer focus: Describe the situation, how you learned what users needed, the actions you took to support change and the result. Include how you responded to feedback or resistance.

Tell me about a time testing or feedback changed your solution.

What to coverSuggested answer focus: Give a real example of what the test or feedback revealed, what you changed and the outcome. Explain the trade-off you made rather than presenting the first design as inevitable.

Tell me about a time you mentored a colleague through an unfamiliar challenge.

What to coverSuggested answer focus: Set out the colleague’s challenge, what you did to support their judgment or capability and what resulted. Focus on the colleague’s growth as well as the immediate delivery outcome.

Tell me about a time you balanced delivery pace with risk concerns.

What to coverSuggested answer focus: Use a real situation to describe the time pressure, the risk you identified, the action you took and the result. Explain how you involved others or adjusted the scope without overlooking the concern.

02

Sharpen your knowledge

Topics to prepare

  • AI product lifecycle and value

    The role owns solutions from concept through adoption and ongoing value delivery. Revise how to define an operational problem, choose a measurable outcome and decide whether a solution remains useful after launch. Exercise: sketch a one-page lifecycle for a hypothetical business-team request, with a baseline, success measure and review point.

  • Agents, skills and automation

    The posting explicitly calls for building AI agents, skills and automation solutions with low-code and no-code tools. Exercise: compare when each approach might fit a hypothetical operational task, including where a person should review the result. The review point is a preparation suggestion, not a stated design requirement.

  • Governance and risk in a regulated environment

    The posting requires a strong understanding of governance and risk requirements and responsible AI use. Exercise: for a hypothetical AI-assisted workflow, identify potential failure, information-handling and oversight questions to resolve before deployment. These are discussion prompts, not claims about Macquarie’s specific controls.

  • Adoption and organisational change

    The role includes supporting change, user engagement and ongoing value delivery across business and technology teams. Exercise: outline how you would distinguish a solution that works in testing from one that users adopt and that improves their work.

Skills to revise

  • Translate stakeholder needs into a solution brief

    Practice turning a hypothetical operational complaint into a concise brief: users, current workflow, problem, proposed AI use, constraints and measurable outcome. This mirrors the stated need to understand stakeholders’ challenges and deliver practical solutions.

  • Configure a low-code or no-code prototype

    Using a platform you already know, practice configuring a small hypothetical agent or automation, then document its inputs, outputs and deployment steps. The posting asks for experience building, configuring and deploying these solutions but names no platform.

  • Plan and run solution tests

    Create a short test set for a hypothetical AI workflow covering expected results, ambiguous requests and failures; record what you would change after user feedback. Testing and user engagement are explicit parts of the role.

  • Present a measurable rollout plan

    Practice a brief stakeholder presentation that connects a proposed solution to a baseline, success measure, adoption steps and a post-launch review. The posting calls for stakeholder influence, change support and ongoing value delivery.

  • Coach a colleague through a design decision

    Rehearse explaining a prototype’s purpose, limitations and risk questions to a colleague, then invite them to critique the design. This prepares for the stated responsibility to mentor colleagues and promote responsible AI practices.

04

Your preparation checklist

  • Select a delivery example

    The role calls for experience building and deploying AI-powered solutions with low-code or no-code tools. Prepare one project walkthrough covering the business problem, your decisions, configuration, deployment and outcome.

  • Practice turning an operational problem into a solution

    The role involves working with business stakeholders to identify operational challenges. As interview practice, draft a short discovery plan with questions, success measures and a proposed AI-powered approach.

  • Sketch an agent or automation design

    The role includes building AI agents, skills and automation solutions. Prepare a simple design showing the workflow, where low-code or no-code tools fit, and how users would interact with it; label it as an illustrative exercise.

  • Gather evidence of measurable value

    The posting emphasizes practical solutions and ongoing value delivery. Prepare a concise account of how you tested a solution, measured its effect and adjusted it after release, using actual results where available.

  • Explain adoption and responsible-use decisions

    The role calls for user engagement, change support and an understanding of governance and risk in a regulated environment. Prepare an example of stakeholder alignment, user testing and risk considerations, without assuming Macquarie uses a particular framework.

  • Prepare a mentoring and influence example

    Senior responsibilities include mentoring colleagues and influencing business and technology teams. Outline a specific situation, the actions you took and the resulting decision or capability improvement.

  • Bring targeted questions

    Ask how the Enterprise AI Enablement team selects opportunities, measures ongoing value, coordinates with business and technology teams, and approaches responsible AI and adoption. These are questions to clarify the role, not assumptions about its processes.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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