Machine Learning Engineer

• 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.

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.