Manager of Machine Learning, Workplace Solutions

Responsibilities:

• Lead and manage a team of machine learning engineers, providing guidance, support, and training to foster professional growth and enhance team performance.

• Oversee the development, productionalization, and deployment of machine learning models, ensuring alignment with project goals and stakeholder requirements.

• Formulate and implement the AI/ML engineering strategy, ensuring it aligns with business goals and technology vision. Supervise the design, development, and deployment of AI/ML models and systems, ensuring they meet performance, scalability, and security standards.

• Collaborate with data science teams to understand data requirements, translating them into machine learning specifications and guiding the team in efficient model development.

• Monitor and optimize existing machine learning pipelines in production, diagnosing and resolving data inconsistencies and model performance issues.

• Engage with internal stakeholders to understand business processes, developing hypotheses and translating requirements into effective analytical solutions.

• Maintain a hands-on approach, contributing to coding and engineering tasks as needed, while promoting best practices in machine learning engineering.

• Prepare and deliver insightful presentations on machine learning initiatives and findings to executive leadership and other stakeholders.

• Actively participate in strategic planning for the machine learning engineering function, aligning with broader organizational goals and technological advancements.

• Ensure compliance with best practices in machine learning development, including version control, testing, and documentation.

The ideal candidate will:

1.Lead and manage a team of machine learning engineers, providing guidance, support, and training to foster professional growth and enhance team performance. Oversee the development, productionalization, and deployment of machine learning models, ensuring alignment with project goals and stakeholder requirements.
2. Formulate and implement the AI/ML engineering strategy, ensuring it aligns with business goals and technology vision. Supervise the design, development, and deployment of AI/ML models and systems, ensuring they meet performance, scalability, and security standards.
3. Maintain a hands-on approach, contributing to coding and engineering tasks as needed, while promoting best practices in machine learning engineering.

Qualifications:

• Undergraduate degree in Computer Science, Data Science, or a related field; a graduate degree is preferred.

• Minimum of 8 years of related work experience, with at least 3 years in a leadership role managing machine learning engineering teams.

• Proven ability to lead and mentor teams, fostering a culture of innovation and continuous improvement.

• Strong understanding of machine learning frameworks and tools (e.g., TensorFlow, PyTorch, Scikit-learn).

• Proficiency in programming languages such as Python (including PySpark, PySQL)  and experience with cloud-based AI services (e.g., AWS, Azure).

• Familiarity with Feature Store Usage, LLMs, Gen AI, RAG, Prompt Engineering and Model Evaluation.

• Experience with API design and development.

• Experience with data engineering principles and technologies, including ETL processes and data pipeline development.

• Knowledge of machine learning lifecycle best practices and the ability to translate business requirements into technical solutions.

• Experience with CI/CD practices for both machine learning and data engineering workflows. 

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.