Machine Learning (ML) Platform Engineer in Toronto, Ontario at PureFacts Financial Solutions

About PureFacts Financial Solutions

PureFacts is the leader in the Revenue Performance Management category for wealth and asset management firms. The PureRevenue™ Platform helps organizations maximize revenue potential by connecting pricing, billing, compensation, advisor behavior, and AI-powered intelligence within a single Revenue Book of Record. By transforming fragmented revenue processes into a coordinated growth system, firms gain greater visibility, stronger pricing discipline, improved revenue capture, and more effective advisor alignment. The result is faster organic growth, improved profitability, and increased enterprise value. For more than 25 years, PureFacts has helped leading financial institutions turn revenue from an operational process into a strategic advantage.

At PureFacts, we are building an AI-native platform and company. We embed AI, intelligent automation, and agentic workflows across our products and operations to detect anomalies, surface insights, streamline repetitive work, and support faster, better decision-making. In a highly regulated industry, we believe AI must be practical, governed, and auditable—amplifying human expertise while helping our teams and clients focus on higher-value, strategic work.

About the role

The Machine Learning Platform Engineer will be responsible for building and scaling the infrastructure that powers AI and machine learning across PureFacts’ platform. This role sits at the intersection of data engineering, platform engineering, and machine learning, ensuring that ML models can be reliably developed, deployed, monitored, and scaled in production environments.

You will play a critical role in enabling PureFacts’ AI-first strategy by creating systems and pipelines that allow teams to deliver AI solutions efficiently, automate workflows, and reduce operational overhead.

What you'll do

AI Infrastructure & Platform Development

Design and build scalable ML infrastructure and platforms to support model development and deploymentDevelop systems that enable rapid experimentation, testing, and deployment of AI modelsCreate reusable frameworks and tooling to standardize ML workflows across teams

MLOps & Model Lifecycle Management

Establish and maintain end-to-end MLOps pipelines, including:Data ingestion and preprocessingModel training and validationDeployment and versioningMonitoring and performance trackingImplement best practices for CI/CD for machine learning systemsEnsure reproducibility, reliability, and traceability of models

Automation & Efficiency

Build systems that automate repetitive ML and data workflows, reducing manual effortEnable teams to deploy and manage models with minimal operational overheadSupport the broader goal of eliminating low-value work through automation and intelligent systems

Data Pipeline & Integration

Develop and maintain robust data pipelines and feature storesEnsure high-quality, scalable data flows for training and inferenceIntegrate ML systems into PureFacts’ SaaS platform and client-facing applications

Cloud & Scalable Systems

Design and manage infrastructure on cloud platforms (Azure-based)Optimize for scalability, performance, and cost efficiencyWork with containerization and orchestration tools (Docker, Kubernetes)

Monitoring, Observability & Reliability

Implement monitoring systems for:Model performance and driftData quality and pipeline healthSystem reliability and uptimeBuild alerting and logging systems to ensure proactive issue detection and resolution

Cross-Functional Collaboration

Partner with data scientists, ML engineers, and product teams to operationalize modelsWork closely with engineering teams to integrate ML systems into production environmentsSupport teams in adopting AI and automation capabilities effectively

Governance & Security

Ensure infrastructure meets security, privacy, and compliance requirementsSupport responsible AI practices through:Model versioning and auditabilityData governance and access controls

Qualifications

Experience

3-5 yrs ML platform engineering for infrastructure, containerization, model serving, monitoring, drift detection, automated retraining pipelinesExperience building and maintaining production-grade ML systemsExperience in SaaS, fintech, or data-driven environments is preferred

Technical Skills

Strong programming skills in Python (required)Experience with:Data processing (SQL, Spark)ML frameworks (TensorFlow, PyTorch, Scikit-learn)MLOps tools (MLflow, Kubeflow, Airflow, etc.)Experience with:Cloud platforms (AWS, Azure, GCP)Containerization (Docker) and orchestration (Kubernetes)CI/CD pipelines and DevOps practices

Infrastructure & Systems Thinking

Strong understanding of distributed systems and scalable architectureExperience building feature stores, model registries, and data pipelinesAbility to design systems for performance, reliability, and maintainability

AI & Automation Mindset

Passion for building systems that enable AI at scale and drive automationFocus on improving efficiency and reducing manual operational workInterest in emerging AI technologies and infrastructure trends

Communication & Collaboration

Strong ability to work across technical and non-technical teamsAbility to explain infrastructure and system design decisions clearlyCollaborative mindset with a focus on team enablement and impact

Education

Degree in Computer Science, Engineering, Data Science, or related fieldAdvanced degree is a plus but not required

Key Success Metrics

Deployment speed and reliability of ML models in productionReduction in manual effort through automation of ML workflowsSystem scalability, uptime, and performanceAdoption of ML infrastructure and tools across teamsEfficiency gains in model development and deployment cyclesThe pay range for this role is:100,000 - 120,000 CAD per year(Toronto, Canada)