Senior Devops / MLops Engineer

Inviol; Auckland; Hybrid; NZD 120,000 - 180,000; Full time

Job summary

inviol is hiring a Senior DevOps / MLOps Engineer to own the infrastructure that runs our AI-driven safety platform. You’ll manage serverless Azure/Cloudflare stacks, edge device fleets, and production ML workflows, shaping architecture and automation across global sites.

You will own deployment pipelines, run multi-arch builds, monitor health, and enable reproducible training and model delivery to production. This role offers real ownership in a growing NZ startup with hybrid Auckland work.

Responsibilities

• Own the infrastructure and IaC across stacks and environments.

Skills

DevOps

SRE

Platform engineering

Python

Containerization

Automation

Edge/IoT

Pulumi

Terraform

Docker

Kubernetes

Tools

Pulumi

Terraform

Docker

Kubernetes

CI/CD

Job description

Jora New Zealand will close on 9th September 2026. Thank you for being with us, we are cheering you on as you continue your career journey.

inviol is one of New Zealand's fastest-growing startups, on a mission to save lives by turning AI insights into safer worksites. We plug into the CCTV cameras a business already has, spot the risky moments no one was watching, and turn them into coaching instead of incident reports. Sites using us cut operational health and safety risk by more than 50% within weeks. Coca-Cola and NZ Post are among the names that trust us with it.

We're hiring a Senior DevOps / MLOps Engineer to own the infrastructure that makes all of it run.

Three moving parts, one owner

Our cloud platform is serverless, Azure and Cloudflare, deployed with Pulumi. Our second production environment is a fleet of edge devices bolted into warehouses, ports and cold stores around the world, running GPU inference on live camera feeds. And the third is the models that gets retrained, re-evaluated and re-shipped to every one of those devices on its own schedule.

What you'll actually do

• Own the infrastructure. Infrastructure as code across stacks & environments. It's yours to shape.
• Make deploys boring. CI/CD everywhere, staged rollout, zero-downtime swaps, rollback that works when it's 4pm on a Friday.
• Run the fleet. Provisioning, imaging, multi-arch container builds, remote config and health monitoring for edge devices you'll never physically touch. Where the stack goes next is a call you'll help make.
• Ship the models. Own the road from trained model to production GPU. Versioning, registry, evaluation gates, staged rollout across the global fleet.
• Build the ML plumbing. Training infrastructure, dataset versioning, data pipelines, experiment reproducibility.
• Watch the models in the wild. Detection rates, false positive trends, per-site and per-camera drift.
• AI Orchestration. Help decide what needs to be deterministic and what doesn't. Explore, experiment and run LLMs, harnesses and automation as a part of workflows in a real AI first environment.
• See everything. Application Insights, OpenTelemetry, Logpush. Alerting that's worth waking up for, and nothing that isn't.

What's in it for you?

• Impact & purpose. Every improvement to uptime is a camera that keeps people safe. The work has a body count it prevents.
• Real ownership. You'll be a senior voice on a small team. No layers to route decisions through.
• Hard problems. Distributed inference on other people's networks is genuinely difficult, and it's ours to solve.
• Growth. Fast-growing NZ startup expanding into Australia and the US, the surface area grows faster than the team does.
• People-first culture. Trust and curiosity, direct access to the founder, and no appetite for process for its own sake.

Who we're looking for

• Experienced. 3+ years in DevOps, SRE or platform engineering, with real production ownership behind you.
• IaC-fluent. Pulumi, Terraform or equivalent.
• Container-deep. Linux, Docker, multi-arch builds, and networking you can reason about under pressure.
• Python-comfortable. Enough to work inside ML tooling and training code, not just around it.
• Pragmatic. You know the difference between the right architecture and the right architecture now.
• An automator. Manual process offends you slightly.
• Edge, IoT or fleet experience

• NVIDIA Jetson, DeepStream, TensorRT or video pipelines (RTSP/ffmpeg)
• computer vision and models
• MLflow, Weights & Biases, DVC or similar
• annotation tooling
• running VLMs/LLMs in production
• Kubernetes deep enough to make an architecture call
• startup experience.

Details

Auckland-based, hybrid, permanent full-time.