Ai Product Owner & Solution Architect For Enterprise Platforms

Saasnine; Melbourne; On-site; AUD 140,000 - 170,000; Full time

Job summary

Saasnine in Melbourne is seeking a Microsoft AI Product Owner / Solution Architect to own the end-to-end lifecycle of AI-enabled solutions, delivering value in small, rapid increments. You’ll sit at the intersection of AI product ownership, Microsoft architecture, and real-world customer problems, shaping solutions across Dynamics 365, Power Platform, and Copilot technologies.

This role emphasizes responsible AI, testing feedback, and enablement assets to help customers adopt and realize value

Qualifications

• Experience owning end-to-end AI-enabled solutions lifecycle.
• Strong product mindset balancing speed, quality and risk.
• Hands-on or architectural AI deployment experience.

Responsibilities

• Own iterative improvement cycle for AI-enabled solutions.
• Design patterns for high-velocity solution iteration.
• Incorporate feedback to drive product and architectural improvements.
• Ensure testing, QA, and validation feedback is incorporated.
• Champion Responsible AI principles in solution design.
• Collaborate with sales, delivery, and technical teams to ensure viability.
• Develop customer enablement assets explaining problem, solution, and value.

Job description

Microsoft AI Product Owner / Solution Architect

A market-leading Microsoft partner at the cutting edge of AI, ERP, Data and Manufacturing technology is building out its AI capability and is seeking a Microsoft AI Product Owner / Solution Architect to own the end-to-end lifecycle of AI-enabled solutions.

This role is for someone who thrives on small, rapid, incremental steps — delivering value early, learning fast, and continuously improving solutions while ensuring quality, consistency, and responsible use.

The Opportunity

You’ll sit at the intersection of AI product ownership, Microsoft architecture, and real-world customer problems.

What This Role Will Actually Do

Own the iterative improvement cycle for AI-enabled solutions, driving continuous refinement and uplift

Design and apply patterns for high-velocity solution iteration, incorporating lessons from deployed technologies

Capture feedback from deployment, testing, and customer usage, and translate it into meaningful product and architectural improvements

Take responsibility for testing, QA, and validation feedback, ensuring solution outputs are:

Consistent

Fit for purpose

Not open to misuse

Ensure AI solutions are built and evolved with Responsible AI principles in mind

Shape and validate customer enablement assets, ensuring they clearly explain:

The problem being solved

How the solution works

How value is delivered in practice

Work closely with sales, delivery, and technical teams to ensure solutions are commercially viable, scalable, and supportable

You’ll design and evolve solutions across the Microsoft ecosystem, including:

Dynamics 365 (ERP / CRM, where relevant)

Agentic AI solutions, including:

AI agents embedded into business workflows

Power Platform (Power Apps, Power Automate)

Ideal Background & Experience Core Experience

Demonstrated ability to capture learning from real-world use and steer solution iterations accordingly

Experience owning solutions beyond initial delivery — including improvement, quality, and enablement

Strong product mindset with the ability to balance speed, quality, and risk

AI & Data Experience

Hands-on or architectural experience with agentic AI development and deployment

Experience working with Microsoft Copilot technologies (M365 Copilot, Copilot Studio)

Practical understanding of how AI behaves in production environments — not just theory

Ways You Might Describe Yourself

AI Product Owner

Microsoft Solution Architect

AI / Data Architect with product responsibility

Technical Product Manager with deep Microsoft expertise

This Role Is Not

A research-only AI role

A one-off PoC or innovation lab role with no ownership

What Success Looks Like (6–12 Months)

A robust, documented process for capturing product feedback and incorporating it into solution iterations

AI solutions that improve over time through structured learning, testing, and validation

Predictable, high-quality outputs that customers trust and understand

Clear enablement approaches that help customers adopt and realise value quickly

A growing portfolio of AI-enabled solutions that can be taken confidently to market

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