Senior MLOps Engineer (AI/ML Pipeline Automation & Kubernetes Deployment)

ai talent • Australia

Visa Sponsorship

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AI Summary

Lead the automation of ML pipelines, model deployment, and production monitoring for an enterprise client. Bridge data science and DevOps to operationalize AI/ML models at scale using Kubernetes, CI/CD, and observability tools. Requires deep expertise in MLOps, containerization, and cloud infrastructure.

Key Highlights

Design and maintain end-to-end ML CI/CD/CT pipelines for scalable model deployment

Deploy and scale containerized ML models on Kubernetes (EKS/AKS/GKE) with high performance

Implement automated monitoring for model drift, decay, and data distribution shifts

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Key Responsibilities

Build, maintain, and scale automated CI/CD/CT pipelines for machine learning models using tools like MLflow or Kubeflow

Package, deploy, and scale high-performance model inference services on Kubernetes (EKS/AKS/GKE) with Triton, TorchServe, or KServe

Provision and maintain ML infrastructure on AWS or Azure using Infrastructure as Code (Terraform)

Implement automated monitoring systems for model decay, feature drift, and data distribution shifts

Maintain centralized model registries, metadata stores, and feature stores (e.g., Feast, SageMaker Feature Store)

Collaborate cross-functionally with data scientists, ML researchers, and cloud platform teams to standardize production paths

Technical Skills Required

Kubernetes Python Machine Learning Operations (MLOps)

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Benefits & Perks

Visa sponsorship (482 On-Hire Sponsorship Transfers, New 482 Visa Sponsorship)

Open to all working visa holders with sponsorship pathway

Full migration and visa support for qualified candidates

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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

Job Description

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.

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