Responsibilities:

• Develop and optimize complex data pipelines, applying machine learning engineering principles to enhance efficiency and scalability.

• Integrate and optimize data and model pipelines within production environments, diagnosing data inconsistencies and documenting assumptions.

• Employ experimental methodologies, statistics, and machine learning concepts to create self-running AI systems for predictive modeling.

• Collaborate with data science teams to review model-ready datasets and feature documentation, ensuring completeness and accuracy.

• Perform data discovery and analysis of raw data sources, applying business context to meet model development needs.

• Comfort with exploratory data exploration and tracking data lineage during inception or root cause analysis.

• Engage with internal stakeholders to understand business processes and translate requirements into analytical approaches.

• Write and maintain model monitoring scripts, diagnosing issues and coordinating resolutions based on alerts.

• Serve as a domain expert in machine learning engineering on cross-functional teams for significant initiatives.

• Stay updated with the latest advancements in AI/ML and apply them to real-world challenges.

• Participate in special projects and additional duties as assigned.

Qualifications:

• Undergraduate degree or equivalent experience; a graduate degree is preferred.

• Minimum of 8 years of relevant work experience.

• At least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g., Glue, Sage Maker).

• Proficiency in programming languages, particularly Python (including Py Spark, PySQL) and familiarity with machine learning libraries and frameworks.

• Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.

• Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.

• Experience with API design and development is a plus.

• Solid understanding of software engineering principles, including design patterns, testing, security, and version control.

• Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.

• Understanding of solution architecture for building end-to-end machine learning data pipelines.

Special Factors Sponsorship

Vanguard is not offering visa sponsorship for this position.

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.