Machine Learning and AI Platform Engineering Specialist, Canada Life

In this role, you’ll be the architect behind Canada Life’s AI and ML platform, crafting and adapting solutions within a cloud environment to perfectly align with ever-evolving business needs. Your expertise will be vital in navigating a complex landscape of requirements, ensuring scalability, maintainability, performance, accuracy, and all the critical aspects that make a truly robust platform. Security, compliance, and cost-effectiveness will also be at the forefront of your considerations.

You’ll work hand-in-hand with the AI Product Owner, software developers, data platform engineers, quality assurance specialists, governance & support experts – a true cross-functional dream team spread across Toronto and other offices. Additionally, you’ll play a vital role in our on-call rotation, ensuring our customers receive the support they need.

Responsibilities:

• Design and build highly scalable machine learning and AI platform solutions within a cloud environment.
• Partner with our internal customers to understand their ML development pain points and craft platform solutions to address them.
• Support the effort to build and enhance MLOPs services, including platform monitoring, platform, and deployment support for the respective pipelines for data delivery.
• Connectivity enablement for relevant models and their data sources.
• Deliver appropriate ML Resource Access Permissions for respective use cases.
• Provide technical mentorship to data platform engineers and interns on the team.
• Help shape the long-term strategy and grow the team.

What you will bring?

• A University or College education in Computer Science, Computer Engineering or a related field or equivalent combination of education and experience
• 5+ years of experience building production machine learning systems or platform components such as feature store, model training framework, ML prediction service etc.
• Strong coding skills in Python
• Strong understanding of engineering and infrastructure best practices, general software development principles with a machine learning software development life-cycle orientation
• Strong experience on Azure Machine Learning and AWS SageMaker, Azure Data Factory, Databricks administration and GitLab.
• Experience managing the entire machine learning lifecycle.
• Desirable experience on Azure Cognitive Services and AWS Rekognition.
• Strong communication skills with an ability to motivate.
• Customer focus and strong relationship management skills
• Ability to quickly adapt to changing priorities and working within ambiguous situations.
• Effective time management, prioritization, and decision-making skills
• Reliability Status security clearance – this is a personnel security status that is required before an employee can gain access to Protected B information, assets or work sites as outlined by the Government of Canada  website