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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 TensorFlow, PyTorch, and Scikit-learn.

+ Strong understanding of cloud platforms, especially AWS SageMaker.

+ 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.