AI Engineer / Permanent / Melbourne / up to $190,000 + super + bonus
About the Company
Our client is a well established, ASX listed FMCG and retail operations business with a national footprint and a multi decade track record.
AI is a core part of how the business runs, and they're building a small, high impact team to lead that work.
They are looking for an AI Engineer to join their Melbourne team.
About the Role
This AI Engineer role is a hands on build and deploy role combining deep technical delivery with a growing stakeholder facing dimension.
You'll design, build and deploy AI solutions that solve real operational problems, acting as the technical anchor between business problems and working systems, and growing into a role shaping AI adoption across the business.
Benefits
$170,000 to $190,000 + super + a bonus
This is a genuine build and own opportunity, not a maintenance role, with real scope to shape how AI is adopted across a large, established business.
You'll work close to the business rather than buried in a back office function, with room to grow into a broader governance and adoption remit as the function matures.
Duties / Key Responsibilities
Design, build and deploy AI models, LLM based tools and agentic workflows, including the evaluation and monitoring needed to trust them in production
Build and maintain scalable AI infrastructure, data pipelines and integrations back into core business systems
Own the full delivery cycle: solution design, build,



testing, validation, deployment and ongoing support
Partner directly with stakeholders to translate requirements into technical deliverables, through UAT and into real adoption
Contribute to AI governance, risk and compliance, keeping documentation and reusable components in good shape so capability scales past one person
Skills and Experience
5+ years across software development, data engineering or AI and automation, with real production experience shipping LLM powered applications
Strong Python skills, including async work, API frameworks and solid testing practices
Practical cloud experience running AI and ML workloads day to day, ideally with Azure, AWS or GCP exposure or certification
Comfort with REST APIs, auth, event driven integration, relational databases and data pipelines
Working knowledge of MLOps, DevOps and solid software engineering practice
A track record of owning delivery end to end, including working directly with non technical stakeholders, ideally in cross functional or agile teams
Clear written and verbal communication, with the ability to explain a technical tradeoff to a non technical audience
Exposure to retail, supply chain or FMCG adjacent AI applications is a plus, as is an interest in AI governance and ethics
Formal study in a relevant field is useful but not the bar.
A solid body of shipped work counts for just as much
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