Senior AIML Engineer

Toronto - Canada

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

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.

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

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...

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.