About the role

The opportunity

This is a chance to take ownership of the infrastructure behind an AI platform used to identify high-risk moments in operational environments. The work spans serverless cloud, globally distributed edge devices, model deployment, monitoring and automation.

It is not a role for someone who wants a narrow lane. You will be close to the product, the models, the infrastructure and the real-world constraints of running systems in environments you cannot physically access.

Company profile

You will be joining a growing New Zealand technology business building practical AI products for workplace safety. Their platform connects into existing camera infrastructure and turns real-time insights into coaching opportunities that help reduce risk.

The team is small, technical and pragmatic. You can expect direct access to decision-makers, low bureaucracy and plenty of room to shape how things are done.

The role

As Senior Software Engineer, you will own and improve the systems that keep the platform reliable, scalable and observable.

You will work across:

• Infrastructure as code across Azure, Cloudflare and Pulumi 
• CI/CD, staged deployments, rollback patterns and zero-downtime releases 
• Provisioning, imaging, configuration and monitoring for remote edge devices 
• Multi-architecture container builds and Linux-based environments 
• Model versioning, evaluation gates and staged rollout to GPU-enabled devices 
• ML infrastructure, dataset versioning, training pipelines and reproducibility 
• Observability across applications, devices and model performance 
• AI workflow orchestration, including where deterministic systems and LLM-based automation make sense 

Your fit

You will bring:

• 3+ years in Software, DevOps, SRE, platform engineering or similar 
• Strong production ownership experience 
• Infrastructure as code experience with Pulumi, Terraform or equivalent 
• Solid Linux, Docker, networking and containerisation skills 
• Python capability, ideally around ML tooling or automation 
• A practical mindset and strong bias toward automation 

Useful extras include:

• Edge, IoT or fleet management experience 
• NVIDIA Jetson, DeepStream, TensorRT, RTSP or ffmpeg 
• Computer vision or model deployment experience 
• MLflow, Weights & Biases, DVC or similar 
• Kubernetes architecture experience 
• Cloudflare Workers, D1, KV or R2 
• Startup experience 

Apply now

If you are looking for a senior engineering role with genuine ownership, practical AI problems and infrastructure that matters in the real world, we would like to hear from you.