Principal ML System Engineer

About the role

PointClickCare is looking for a Principal AI Machine Learning Platform Engineer to set the technical vision and strategy for the machine learning platform that powers ML and generative AI development across the company. The role works with Product and Engineering leadership to define reference architectures, standards and roadmaps for pipelines, tooling and infrastructure used for model training, deployment, serving and monitoring, and provides technical leadership and mentorship. The team acts as product owner for the ML platform, working with other engineering teams and horizontal partners to deliver safe, scalable AI products.

Key responsibilities

• Partner with product and engineering leadership to turn business and product objectives into a multi-quarter technical strategy and roadmap for the ML platform.
• Define reference architectures and standards for scalable data and ML pipelines covering training, evaluation, deployment and serving.
• Set the direction and best practices for MLOps across the company, including CI/CD for models, model registry, feature stores and experiment tracking, and drive build-vs-buy decisions.
• Establish practices for reliability, observability and performance of ML systems in production, including monitoring, alerting and automated remediation.
• Define the security architecture, including authentication, role-based access control, audit logging and compliance monitoring.
• Define secure, cost-efficient integration patterns with existing systems, APIs and data sources at scale.
• Provide technical leadership and mentorship to senior engineers and influence the org-wide technical roadmap for ML infrastructure.

Required profile

• Expert-level Python and Java with strong software engineering fundamentals.
• Demonstrated track record of setting technical direction and driving org-wide initiatives across multiple teams.
• Preferred: Bachelor's degree or higher in Computer Science, Machine Learning or a related field.
• Preferred: experience leading the build-out of critical cross-team systems and implementing security at scale.

Required skills

• Deep experience designing ML platforms and ML Ops workflows at scale, with tools such as MLFlow, Kubeflow, Ray and model-serving frameworks.
• Cloud platforms (AWS, Azure and/or GCP), containerization and orchestration (Docker, Kubernetes).
• Preferred: Azure Machine Learning, Databricks, serverless environments and LLM serving optimization.

Remote roles involve occasional travel to the Mississauga and/or Salt Lake City offices for onboarding, team events and meetings. Hybrid roles require living within commuting distance of the listed location.