MLOps Engineer

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 practice across a growing data function, this is a strong opportunity to make a tangible impact. What you’ll be doing; • Design, build and maintain scalable ML pipelines that automate training, testing, deployment and monitoring. • Work closely with data scientists to productionise models-ensuring they’re secure, reliable and ready for real‑world load. • Develop and maintain CI/CD workflows that support rapid iteration, versioning and safe deployment of ML assets. • Manage cloud and on‑prem environments for ML workloads, including compute, storage and model‑serving infrastructure. • Implement monitoring, logging and alerting to track model performance, detect drift and ensure high availability. • Partner with engineering, data and IT teams to understand requirements and deliver solutions that meet both technical and business needs. • Embed best practices for data security, model governance and compliance across the ML lifecycle. • Maintain clear documentation and contribute to internal MLOps standards, patterns and reusable components. • Stay across emerging tools and techniques, helping evolve the MLOps stack and uplift engineering maturity. What you’ll bring; 3+ years across MLOps, ML engineering, DevOps or related fields, with hands‑on experience taking ML products into production. – Strong programming skills in Python, Go, Rust or similar – Experience with ML frameworks: TensorFlow, TensorRT, PyTorch, scikit‑learn – Solid understanding of DevOps practices: CI/CD, IaC, Docker, Kubernetes – Familiarity with data engineering concepts, ETL and preprocessing – Experience with monitoring tools: Prometheus, Grafana, ELK – Exposure to ML lifecycle tools such as ClearML – Strong Git and collaborative development practices Mane Consulting specialise in Data & Analytics. For more information on this role or others like this in the market apply below or contact Mark.cornwel-smith @mane.com.au