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 quick-moving, research-heavy settings
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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