Senior Machine Learning Systems Engineer

Team Summary: This team will serve as the product owner for the machine learning platform capabilities within PointClickCare, working closely with other engineering teams across the organization to identify, build, and support traditional machine learning (ML) and hybrid ML/LLM solutions. This centralized team with deep specialization will closely integrate with key horizontal partners to ensure delivery of safe, scalable, and high-impact AI products.

Job Summary: The Senior Machine Learning Systems Engineer will work closely with the Product and Engineering teams to design, build, and operate the machine learning platform that enables teams across PointClickCare to develop, deploy, and scale ML solutions. The Senior AI Machine Learning Systems Engineer will also build and maintain the pipelines, tooling, and infrastructure for model training, deployment, serving, and monitoring that underpin our AI products.

Key Responsibilities:

• Collaborate with product and engineering teams to translate ML needs into reliable, reusable platform capabilities.
• Design and build scalable data and ML pipelines that support model training, evaluation, deployment, and serving.
• Develop and maintain ML Ops tooling and workflows, including CI/CD for models, model registry, feature stores, and experiment tracking.
• Ensure the reliability, observability, and performance of ML systems in production through monitoring, alerting, and automated remediation.
• Implement comprehensive security mechanisms for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring.
• Securely integrate the platform with existing systems, APIs, and data sources with secure communication protocols, and optimize infrastructure for cost, performance, and scale.
• Mentor engineers on the teams and promote reusable platform patterns and best practices.

Qualifications & Skills:

• Expert level in Python and Java, and strong software engineering fundamentals.
• Experience designing and building ML platforms and MLOps workflows, with familiarity with tools such as MLflow, Kubeflow, Ray, and model-serving frameworks.
• Experience with cloud platforms (primarily Azure, secondarily AWS and GCP).
• Experience with ML runtime containerization, optimization, and orchestration (Docker, Kubernetes).

Preferred:

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field.
• Working familiarity with Azure Machine Learning components and Databricks processing and serverless environments.
• Experience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring.
• Experience optimizing large model training and inference (including LLM serving) for performance and cost.