Job Description

Job Description

Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment.

Core Responsibilities

• Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.

• Develop and implement robust  MLOps pipeline  to support the deployment, monitoring, and lifecycle management of AI/ML models in production environments.

• Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.

• Collaborate closely with data scientists to validate model-ready datasets and ensure thorough, accurate feature documentation.

• Conduct exploratory data analysis and discovery on raw data sources, incorporating business context to support model development.

• Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.

• Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.

• Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolutions.

• Act as a subject matter expert in machine learning engineering on cross-functional teams, contributing to high-impact initiatives.

• Stay current with advancements in AI/ML and evaluate their applicability to business challenges.

Qualifications:

• Bachelor’s degree in a relevant field required; Master’s degree preferred.

• 6+ years of relevant experience in data engineering or machine learning engineering.

• Minimum 3 years of hands-on experience building ETL pipelines using AWS services

• Proven experience developing and implementing  MLOps pipeline  for deploying, monitoring, and managing AI/ML models in production.

• Proficient in Python and familiar with key machine learning frameworks and libraries.

• Strong understanding of cloud technologies and AI/ML platforms like AWS SageMaker.

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

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

• Experience designing and implementing end-to-end machine learning pipelines and solution architectures.

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.

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