Senior Machine Learning Systems Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Act as the product owner for machine learning platform functionalities within PointClickCare.

• Collaborate with engineering teams to identify, develop, and support both traditional ML and hybrid ML/LLM solutions.

• Architect, construct, and manage the machine learning platform that empowers teams to create, deploy, and scale ML solutions.

• Develop and sustain pipelines, tools, and infrastructure for model training, deployment, serving, and monitoring.

• Convert ML requirements into dependable, reusable platform functionalities.

• Design and construct scalable data and ML pipelines for training, evaluation, deployment, and serving of models.

• Create and manage MLOps tools and workflows, such as model CI/CD, model registry, feature stores, and experiment tracking.

• Guarantee the reliability, observability, and performance of production ML systems through monitoring, alerting, and automated remediation.

• Implement security measures 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.

• Optimize infrastructure for cost efficiency, performance, and scalability.

• Mentor engineers and advocate for reusable platform patterns and best practices.

⛳️ Requirements

• Expert proficiency in Python and Java.

• Strong foundation in software engineering principles.

• Proven experience in designing and developing ML platforms and MLOps workflows.

• Familiarity with MLflow, Kubeflow, Ray, and various model-serving frameworks.

• Experience with cloud services, mainly Azure, with secondary experience in AWS and GCP.

• Experience in ML runtime containerization, optimization, and orchestration using Docker and Kubernetes.

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related discipline (Preferred).

• Working knowledge of Azure Machine Learning components and Databricks processing and serverless environments (Preferred).

• Experience in implementing security at scale, including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring (Preferred).

• Experience in optimizing large model training and inference, including LLM serving, for improved performance and cost efficiency (Preferred).

🏝️ Benefits

• 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.

• In-office events including onboarding, team events, and semi-annual and annual team meetings.