MLOps Engineer
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Summary
An MLOps Engineer in Melbourne who owns the end-to-end ML lifecycle: building scalable pipelines for training, deployment and monitoring, CI/CD workflows, and cloud/on-prem model-serving infrastructure alongside data scientists and platform teams. Core stack spans Python/Go/Rust, TensorFlow/PyTorch, Docker, Kubernetes and Prometheus/Grafana.
Become part of a dynamic team setting new standards in intelligence services, data and AI technologies. Take machine learning out of notebooks and into real‑world production environments. In this role, you'll own the end‑to‑end ML lifecycle, working with data scientists, software engineers and platform teams to ensure models are deployed reliably, monitored effectively and scaled with confidence. If you enjoy building robust pipelines, automating the messy parts of ML delivery, and shaping best…