Job Description We are seeking a highly skilled Senior Machine Learning Engineer with a strong focus on Machine Learning Operations (MLOps) to join our innovative data science team.
As a Senior Machine Learning Engineer, you will be responsible for designing, developing, and deploying scalable machine learning pipelines into production environments.
You will work closely with data scientists and software engineers to optimise model deployment pipelines, ensure continuous integration/continuous delivery (CI/CD), and maintain model performance in a live setting.
Day to day: Design, implement, and maintain machine learning pipelines for model training, validation, and deployment.
Automate end-to-end model lifecycle management, including data preprocessing, model training, testing, monitoring, and updates.
Collaborate with data engineering teams to build scalable, resilient, and secure infrastructure for ML models in production.
Ensure CI/CD practices for model deployment, including version control, testing, and rollback strategies.
Monitor model performance, identify bottlenecks, and implement improvements to maintain optimal results.
Develop tools and frameworks for the rapid deployment and iteration of machine learning models.
Optimise resource usage and cost by ensuring efficient model inference and serving architectures.
Maintain and improve data pipelines, ensuring data quality, availability, and integrity.
Collaborate with cross-functional teams to understand business needs and translate them into actionable ML solutions.
Ensure compliance with data privacy and security standards in model handling and deployment.
Qualifications Proficiency in Python and TensorFlow, PyTorch, or other relevant ML libraries.
Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud for ML deployment.
Strong understanding of CI/CD pipelines, containerisation (Docker, Kubernetes), and orchestration tools.
Experience with monitoring tools like Datadog, Grafana, or similar to track model performance.
Knowledge of infrastructure-as-code tools like Terraform or CloudFormation.
Experience with version control (Git) and workflow automation.
Familiarity with distributed data systems like Spark, Hadoop, or Kubernetes.
Strong problem-solving skills and a commitment to continuous learning.
Excellent communication skills, both written and verbal.
Experience working across different time zones Ability to work in a hybrid model (combination of on-site and working from home) Open to working outside standard hours to collaborate with teams across Australia and the UK.
Nice to have: Experience with A/B testing and model validation techniques.
Knowledge of feature store management and model registry systems.
Additional Information You'll be joining a highly motivated, agile team where your ideas and work will directly influence the direction and progress of an expanding global company in a hyper-growth phase.
We pride ourselves on our collaborative and driven culture and offer opportunities for advancement to high achievers.
Other benefits include: Unlimited access to SAGED courses and more through the Greenhouse learning platform, fostering continuous growth and development.
Mental health support through our wellbeing platform, Unmind.
Being part of one of the fastest-growing industries in Australia, improving the lives of hundreds of thousands of patients.
#LI-LL1 #LI-Remote We are committed to facilitating a barrier-free recruitment process and work environment.
If you require any accommodations, we welcome you to let us know so we can work with you to participate fully in our recruitment experience.