Job Description

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

• Bachelors degree in Computer Science Engineering or related field (Masters 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

About Vanguard

At Vanguard we dont just have a missionwere 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.

Required Experience:

Senior IC

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