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Full timeTechnology

Staff Engineer - ML/AI

NuageMelbourne, Melbourne🇦🇺AustraliaPosted 5 Oct 2026

Why this role stands out

This Staff Engineer role offers significant technical ownership and the chance to shape the future of production GenAI, agents, and MLOps within a leading product company. You'll thrive here if you're a seasoned engineer with a passion for hands-on ML/AI development and a desire to influence technical direction while remaining deeply technical, all within a flexible hybrid work environment. This is an excellent opportunity to advance your career by driving impactful AI innovations.

Quick overview

SeniorityMid Senior
Employment typeFull Time
Work modeHybrid
LocationMelbourne, Melbourne, Australia
AWSMLOpsSnowflakeAzureDatabricksGenerative AILLM

Job description

  • Staff-level IC with real technical ownership
  • Production GenAI, agents & MLOps
Staff Engineer - ML/AI

Permanent Melbourne Hybrid

We're working with a major product company looking for a hands on Staff Engineer to provide technical leadership across its growing ML, MLOps and Generative AI capability.

This is a senior individual contributor role with no people management. You'll work across different engineering teams, helping set technical direction while remaining close to the technology and delivery.

What you'll be working on
  • Building and evolving MLOps capabilities across feature stores, model lifecycle, drift detection, monitoring and observability.
  • Designing production GenAI applications, LLM pipelines, agentic workflows and guardrails.
  • Setting engineering patterns and helping shape the technical roadmap across ML and AI.
  • Providing technical leadership and influencing senior and Principal Engineers.
  • Working on established AI products already delivering measurable improvements to customer and internal workflows.
What we're looking for

You'll ideally come from either a software engineering background with strong ML/AI experience, or a data science/ML background that has evolved into production engineering.

We're particularly interested in experience across:

  • Production ML and MLOps.
  • GenAI, LLM workflows and AI agents.
  • Strong software engineering fundamentals.
  • Model monitoring, observability and lifecycle management.
  • Databricks, AWS, SageMaker, Snowflake, Azure or similar platforms.
  • Technical leadership without moving away from hands on engineering.

The environment is primarily Databricks and AWS, although exact tool matching isn't essential. Strong engineering and ML/AI fundamentals matter more.

This would suit someone who wants to remain deeply technical while having genuine ownership over how ML and AI solutions are designed and delivered.

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