There’s a class of machine intelligence that never gets talked about.
It understands reality - in motion, in real-time, in environments where failure isn’t an option.
Our client is a small, well-funded team is building exactly that.
They’re looking for a DevOps / Software Engineer who understands that testing machine learning systems isn’t just about pipelines — it’s about truth, accuracy, and trust at scale.
What you’ll be doing
- Designing and evolving testing and validation environments for complex vision systems
- Building pipelines that go beyond CI/CD, thinking in terms of data quality, drift, and model behaviour
- Working with engineers and researchers to stress-test models against reality
- Tracking subtle shifts in performance, accuracy, latency, consistency, and understanding why they change
- Bringing structure to systems that are constantly evolving
This is not generic DevOps.
You’ll be operating at the intersection of infrastructure, data, and perception.
What they’re looking for
- Solid DevOps foundation (automation, CI/CD, containerisation, testing frameworks)
- Experience working with ML / data / vision systems, or curiosity to go deep
- Someone who notices small anomalies and asks hard questions
- Comfort working in fast-moving, research-heavy environments
- A mindset that leans toward systems thinking over tooling
Why this role is different
- You’re not supporting software teams; you’re shaping how intelligence is validated
- The systems you’ll work on interact with the real world, not just users
- The problems don’t have clean documentation - only signals, outputs, and edge cases
If you’re the kind of engineer who wants to understand how machines actually perceive the world, not just deploy models, this is worth a conversation.
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📌 DevOps Engineer / V&V Software Engineer (City of Sydney)
🏢 provenio people
📍 City of Sydney
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