MLOps Engineer (Machine Learning Operations)

We are partnering with an enterprise client to accelerate their artificial intelligence delivery and operationalise machine learning models at scale. We are seeking a skilled MLOps Engineer to represent our organisation and take technical ownership of continuous training pipelines, model deployment infrastructure, and production monitoring platforms.

In this role, you will bridge data science and DevOps within our client’s ecosystem. You will be instrumental in transforming experimental ML and Generative AI models into reliable, production-grade services through automated CI/CD pipelines, container orchestration, and continuous drift monitoring.

🌏 Visa & Sponsorship Options

As the employer of record, we provide full visa and migration support for qualified engineering talent deployed to our clients:

• 482 On-Hire Sponsorship Transfers: Fully supported for qualified candidates currently in Australia on an existing 482 visa looking to transfer sponsorship to work with our clients.
• New 482 Visa Sponsorship: Available for qualified candidates meeting the commercial experience and technical requirements.
• Temporary & Working Visa Holders: Open to all working visa holders seeking a direct pathway to employer sponsorship.
  

Core Responsibilities

• ML Pipeline Automation: Build, maintain, and scale end-to-end continuous integration and continuous delivery (CI/CD/CT) pipelines for machine learning models using tools such as MLflow, Kubeflow, or Argo Workflows.
• Container & Model Orchestration: Package, deploy, and scale high-performance model inference services on Kubernetes (EKS/AKS/GKE) using tools like Triton, TorchServe, or KServe.
• Infrastructure as Code (IaC): Provision and maintain reproducible ML infrastructure on AWS or Azure using Terraform.
• Model Observability & Drift Detection: Implement automated monitoring systems to detect model decay, feature drift, and data distribution shifts in production.
• Feature Store & Registry Management: Maintain centralized model registries, metadata stores, and feature stores (e.g., Feast, SageMaker Feature Store).
• Cross-Functional Collaboration: Partner closely with data scientists, ML researchers, and cloud platform teams to establish standardized paths to production.

Selection Criteria

• MLOps & DevOps Mastery: Proven commercial experience designing and maintaining automated ML pipelines and production infrastructure.
• Containerisation & Kubernetes: Deep hands-on experience deploying and scaling containerized workloads with Docker and Kubernetes.
• ML Platforms & Tooling: Strong familiarity with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, or Azure ML).
• Software & Scripting: High proficiency in Python and solid scripting skills for automation (Bash/Git).
• Location Requirements: Currently residing in Australia with valid work rights or eligibility for 482 visa sponsorship/transfer.
  

Preferred Qualifications (Nice to Have)

• Experience deploying large language models (LLMs), RAG architectures, or vector search infrastructure.
• Relevant cloud certifications (AWS Machine Learning Specialty / Azure AI Engineer).
• Familiarity with automated compliance, model governance, and security auditing for AI.