Core Responsibilities 

• Design, build, and maintain end-to-end machine learning pipelines from research through production deployment. 
• Engineer scalable training, inference, and retraining workflows using AWS SageMaker. 
• Develop and maintain feature engineering, feature storage, and data preparation pipelines. 
• Automate model deployment, testing, validation, and release processes using CI/CD practices. 
• Build batch, real-time, and event-driven architectures. 
• Implement model monitoring for performance, drift detection, data quality, and operational health. 
• Partner with quantitative researchers and data scientists to productionalize research models. 
• Manage model versioning, lineage tracking, experiment management, and reproducibility. 
• Optimize model performance, scalability, reliability, and cloud cost efficiency. 
• Establish engineering standards, testing frameworks, and governance controls for ML solutions. 
• Support production operations, incident response, and continuous improvement of deployed models. 

Required Qualifications:

• Minimum of eight years related work experience, with at least three years of development experience.
• Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
• Experience in software engineering, machine learning engineering, data engineering, or a related technical discipline. 
• Strong experience building and deploying machine learning solutions in production environments. 
• Expertise in Python and modern data science libraries (Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, or similar). 
• Hands-on experience with AWS services, including SageMaker 
• Experience building and maintaining machine learning pipelines, feature engineering workflows, and model deployment processes. 
• Knowledge of MLOps practices, including CI/CD, model versioning, experiment tracking, monitoring, and automated retraining. 
• Strong understanding of software development lifecycle practices, testing strategies, and production support. 
• Ability to work effectively with researchers, data scientists, and business stakeholders to deliver business outcomes. 

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Special Factors

Sponsorship

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.