About the role

Senior ML Systems Engineer building scalable ML platforms, pipelines, and MLOps infrastructure for PointClickCare’s healthcare technology platform. Securing, monitoring, and optimizing AI systems in production.

Responsibilities

Serve as product owner for machine learning platform capabilities
Work with engineering teams to identify, build, and support traditional ML and hybrid ML/LLM solutions
Design, build, and operate the machine learning platform enabling development, deployment, and scaling of ML solutions
Build and maintain pipelines, tooling, and infrastructure for model training, deployment, serving, and monitoring
Translate ML needs into reliable, reusable platform capabilities
Design and build scalable data and ML pipelines for model training, evaluation, deployment, and serving
Develop and maintain MLOps tooling and workflows, including model CI/CD, model registry, feature stores, and experiment tracking
Ensure production ML system reliability, observability, and performance through monitoring, alerting, and automated remediation
Implement authentication, role-based access control, audit logging, and compliance monitoring
Integrate the platform securely with systems, APIs, and data sources
Optimize infrastructure for cost, performance, and scale
Mentor engineers and promote reusable platform patterns and best practices

Expert level in Python and Java
Strong software engineering fundamentals
Experience designing and building ML platforms and MLOps workflows
Familiarity with MLflow, Kubeflow, Ray, and model-serving frameworks
Experience with cloud platforms, primarily Azure and secondarily AWS and GCP
Experience with ML runtime containerization, optimization, and orchestration using Docker and Kubernetes
Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field (preferred)
Working familiarity with Azure Machine Learning components and Databricks processing and serverless environments (preferred)
Experience implementing security at scale, including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring (preferred)
Experience optimizing large model training and inference, including LLM serving, for performance and cost (preferred)

Benefits starting from Day 1
Retirement Plan Matching
Flexible Paid Time Off
Wellness Support Programs and Resources
Parental & Caregiver Leaves
Fertility & Adoption Support
Continuous Development Support Program
Employee Assistance Program
Allyship and Inclusion Communities
Employee Recognition
Bonus
Flexible work arrangements
In-office events including onboarding, team events, and semi-annual and annual team meetings