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Heidi Logo Heidi · IT - Software Development

Senior LLMOps Engineer

Australia, Sydney · Job Posted February 18, 2026
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Job Description

Working closely with our Engineering Manager, you’ll be a Senior LLMOps Engineer on the Model Platform team. You are a technical leader responsible for building and scaling the infrastructure that powers our entire model lifecycle. Your mission is to build a robust, scalable, and reliable platform for deploying and managing our LLMs. You will lead the design and implementation of our LLMOps strategy, ensuring our AI engineers can move models from development to production seamlessly and efficiently. You will combine your deep infrastructure knowledge with MLOps principles to solve the critical challenges of serving models at scale.

Job Responsibility

  • Lead the architecture, design, and implementation of our end-to-end LLMOps platform, from data ingestion and model training pipelines to production deployment and monitoring
  • Build and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines to automate the testing, validation, and deployment of large language models
  • Engineer highly available and scalable model serving solutions using modern infrastructure like Kubernetes, ensuring low latency and high throughput for our production services
  • Collaborate closely with AI research and engineering teams to understand their needs, streamline workflows, and create the tooling that accelerates their development cycles
  • Champion and implement best practices for model versioning, experiment tracking, monitoring, and governance across the organization
  • Mentor mid-level and junior engineers, sharing your deep expertise in infrastructure, automation, and operational excellence to foster a culture of reliability and scalability

Requirements

  • Proven track record of designing, building, and maintaining MLOps or LLMOps infrastructure in a production environment
  • Previous hands-on experience building scalable, cloud-native infrastructure and platforms
  • Deployed and managed large-scale machine learning models in a production environment
  • Expert in Python, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and Infrastructure as Code (e.g., Terraform, CloudFormation)
  • Deep and practical understanding of the entire machine learning lifecycle and the specific operational challenges of large language models
  • Ability to translate complex engineering and research requirements into concrete, robust, and automated platform solutions
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Nice to have

  • Experience with advanced model serving and optimization techniques (e.g., quantization, distillation, multi-model serving)
  • Experience with specialized MLOps frameworks like MLflow, Kubeflow, or Weights & Biases
  • Contributions to open-source MLOps or infrastructure-related projects

What we offer

  • Flexible hybrid working environment, with 3 days in the office
  • Additional paid day off for your birthday and wellness days
  • Special corporate rates at Anytime Fitness in Melbourne, Sydney tbc
  • A generous personal development budget of $500 per annum
  • Learn from some of the best engineers and creatives, joining a diverse team
  • Become an owner, with shares (equity) in the company
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