About the role
Vanguard’s Corporate Services 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
-
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.
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.
About Vanguard Canada
Putting investors first since 1975:
We sell high-quality investment products at the lowest possible price. This is the core of our investment philosophy because we think it’s essential to your investment success.
Unique structure: Vanguard was founded on a simple but revolutionary idea: that an investment company should be run for the sole benefit of its investors. At Vanguard, we don’t have any outside owners, so we don’t need to worry about a stock price or about generating profits for outside owners. This helps us keep costs low, so more of your money stays in your pocket—where it belongs.
A pioneer in the industry: 30 million investors worldwide trust us with over $7 trillion of their savings. Together we’re changing the way the world invests.
About the role
Vanguard’s Corporate Services 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
-
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.
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.
About Vanguard Canada
Putting investors first since 1975:
We sell high-quality investment products at the lowest possible price. This is the core of our investment philosophy because we think it’s essential to your investment success.
Unique structure: Vanguard was founded on a simple but revolutionary idea: that an investment company should be run for the sole benefit of its investors. At Vanguard, we don’t have any outside owners, so we don’t need to worry about a stock price or about generating profits for outside owners. This helps us keep costs low, so more of your money stays in your pocket—where it belongs.
A pioneer in the industry: 30 million investors worldwide trust us with over $7 trillion of their savings. Together we’re changing the way the world invests.