Senior LLMOps Engineer

Summary

Join Heidi, a health-tech company on a mission to revolutionize healthcare delivery, as a Senior LLMOps Engineer. You will be a technical leader, building and scaling the infrastructure for our entire model lifecycle. Lead the design and implementation of our LLMOps strategy, ensuring seamless and efficient model deployment. Collaborate with AI and engineering teams, championing MLOps best practices. Mentor junior engineers and contribute to a culture of reliability and scalability. This role requires proven experience in MLOps/LLMOps infrastructure, scalable cloud-native infrastructure, and large-scale machine learning model deployment. A Bachelor's or Master's degree in a related field or equivalent experience is needed.

Requirements

• You’ve a proven track record of designing, building, and maintaining MLOps or LLMOps infrastructure in a production environment
• You’ve previous hands-on experience building scalable, cloud-native infrastructure and platforms
• You’ve deployed and managed large-scale machine learning models in a production environment, with a deep understanding of the associated challenges
• You are considered an expert in Python, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and Infrastructure as Code (e.g., Terraform, CloudFormation)
• You have a deep and practical understanding of the entire machine learning lifecycle and the specific operational challenges of large language models
• You have the ability to translate complex engineering and research requirements into concrete, robust, and automated platform solutions
• A Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Responsibilities

• Lead LLMOps Platform Development: 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
• Automate the LLM Lifecycle: 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
• Ensure Scalable and Reliable Deployment: Engineer highly available and scalable model serving solutions using modern infrastructure like Kubernetes, ensuring low latency and high throughput for our production services
• Partner with AI and Engineering Teams: Collaborate closely with AI research and engineering teams to understand their needs, streamline workflows, and create the tooling that accelerates their development cycles
• Establish MLOps Best Practices: Champion and implement best practices for model versioning, experiment tracking, monitoring, and governance across the organization
• Mentor and Guide: Mentor mid-level and junior engineers, sharing your deep expertise in infrastructure, automation, and operational excellence to foster a culture of reliability and scalability

Preferred Qualifications

• 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

Benefits

• 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, if Heidi wins, we all win
• The rare chance to create a global impact as you immerse yourself in one of Australia’s leading healthtech startups
• If you have an impact quickly, the opportunity to fast track your startup career!