Vanguard
Vanguards Corporate Services - CAI department is seeking a Senior AI/ML Engineer to design and deliver scalable machine learning infrastructure and pipelines that enable experimentation, deployment, and monitoring of AI/ML models across the enterprise.
This role is ideal for someone with deep technical expertise in building production-grade ML systems and a passion for driving innovation through data and automation.
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 Vanguard is not offering visa sponsorship for this position.
About Vanguard At Vanguard, we don't just have a missionwe'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.
This role is ideal for someone with deep technical expertise in building production-grade ML systems and a passion for driving innovation through data and automation.
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 Vanguard is not offering visa sponsorship for this position.
About Vanguard At Vanguard, we don't just have a missionwe'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.