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.