PointClickCare is hiring a Senior ML Systems Engineer CAD in Mississauga!
PointClickCare, a founder-led health tech company serving long-term and post-acute care providers, is hiring a Senior Machine Learning Systems Engineer for its centralized ML platform team. This group acts as product owner for machine learning capabilities across the organization, partnering with engineering and product teams to identify, build, and support both traditional ML and hybrid ML/LLM solutions. The role is hybrid, based out of Mississauga, and requires living within commutable distance of the office for regular team events. You would be a senior technical contributor responsible for the infrastructure that lets other teams at PointClickCare develop, deploy, and scale AI products safely and efficiently.
Key responsibilities
- Translate machine learning needs from product and engineering partners into reliable, reusable platform capabilities
- Design and build scalable data and ML pipelines covering training, evaluation, deployment, and serving
- Develop MLOps tooling and workflows, including model CI/CD, model registries, feature stores, and experiment tracking
- Ensure production ML systems are reliable, observable, and performant through monitoring, alerting, and automated remediation
- Implement platform security such as authentication, role-based access control, audit logging, and compliance monitoring
- Integrate the platform with existing systems, APIs, and data sources using secure communication protocols, and tune infrastructure for cost, performance, and scale
- Mentor engineers and promote reusable platform patterns and best practices
Key requirements
- Expert-level Python and Java with strong software engineering fundamentals
- Experience designing ML platforms and MLOps workflows, with familiarity with tools such as MLflow, Kubeflow, Ray, and model-serving frameworks
- Hands-on cloud experience, primarily Azure with AWS and GCP as secondary
- Experience with ML runtime containerization, optimization, and orchestration using Docker and Kubernetes
- Preferred: bachelor's degree or higher in computer science, machine learning, or a related field
- Preferred: familiarity with Azure Machine Learning components and Databricks processing and serverless environments
- Preferred: security at scale, including RBAC, multi-factor authentication, network security practices, and compliance monitoring
- Preferred: optimizing large model training and inference, including LLM serving, for cost and performance
- Seniority: Senior
- Work mode: Hybrid
- Key skills: Python, Java, MLOps, MLflow, Kubeflow, Ray, Azure, AWS, GCP, Docker, Kubernetes, Azure Machine Learning, Databricks, LLM serving, RBAC
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