Responsibilities

• Architect and implement scalable, efficient, and reliable data and ML pipelines using best practices in machine learning engineering.

• Build and maintain MLOps frameworks to support model deployment, monitoring, and lifecycle management in production environments.

• Ensure data integrity, proactively identifying and resolving quality issues across data and model pipelines.

• Collaborate with data scientists, solution architects, product managers, and Agile leads to align on technical direction and keep stakeholders informed.

• Conduct exploratory data analysis and integrate business context to inform modeling strategies.

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

• Translate business requirements into scalable AI/ML solutions in partnership with internal stakeholders.

• Implement and maintain model monitoring, including data and model drift detection, alerting, and resolution workflows.

• Design and execute A/B testing,backtesting, and other validation strategies to assess model performance and business impact.

• Anticipate ambiguity in data, requirements, or business context and devise creative, scalable solutions to address them.

• Serve as a technical expert in machine learning engineering on cross-functional teams.

• Stay current with advancements in AI/ML and assess their relevance to business challenges.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).

• 8+ years of experience across machine learning engineering,data engineering, and MLOps implementation, including:

Designing and deploying production-grade ML systems.

• Building scalable data pipelines and ML workflows.

• Managing model lifecycle in cloud environments.

• Proficient in Python and familiar with ML frameworks such as Tensor Flow,Py Torch, and Scikit-learn.

• Strong understanding of cloud platforms, especially AWS Sage Maker.

• Experience with CI/CD,containerization(e.g., Docker), and orchestration tools (e.g., Kubernetes).

• Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.

• Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.

• Strong communication and collaboration skills, with experience working across technical and business teams.

• Ability to anticipate ambiguity and devise scalable solutions to address it.

Nice to Have

• Experience with Databricks for scalable data and ML workflows.

• Familiarity with Feature Store concepts and implementation.

• Exposure to real-time prediction systems and streaming data architectures.

• Knowledge of data governance,model explainability, and responsible AI practices.

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.