Senior Machine Learning Engineer

Join a dynamic team supporting model development and operations for research and insights across Investment Management. This role partners closely with quantitative researchers, data scientists, and investment teams to engineer, deploy, and operate production-grade machine learning models that drive research, analytics, and business insights. You will be responsible for building and maintaining the end-to-end ML lifecycle, including model pipelines, feature engineering workflows, automated training and deployment processes, model monitoring, and production operations. The ideal candidate combines strong software engineering fundamentals with hands-on experience implementing MLOps best practices and operating machine learning solutions on AWS SageMaker. Expertise in Python, cloud-native architectures, and scalable data processing is essential.

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.

• 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.

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.