Heidi - Senior LLMOps Engineer

• 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. • Lead LLMOps Platform Development: • 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. • Automate the LLM Lifecycle: • 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. • Ensure Scalable and Reliable Deployment: • 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. • Partner with AI and Engineering Teams: • Establish MLOps Best Practices: Champion and implement best practices for model versioning, experiment tracking, monitoring, and governance across the organization. • Establish MLOps Best Practices: • 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. • Mentor and Guide: • What we will look for: • 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. • Bonus: • 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. • The way we work • The way we work • 1. Build to Last • 1. Build to Last • We design for safety and reliability so clinicians, patients, and our teams can trust what we build every day. • 2. Own Your Practice • Ideas rise on merit, not title, and everyone shares responsibility for the standards we set together. • 3. Move Fast, Stay Steady • We move quickly but never at the cost of trust. Progress only matters if people can depend on what we make. • 4. Make Others Better • Honest feedback, steady support, and shared growth keep our teams improving together. • Flexible hybrid working environment, with 3 days in the office. • 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! • Heidi is dedicated to creating an equitable, inclusive, and supportive work environment that brings people together from diverse backgrounds, experiences, and perspectives. Our strength is in our differences.