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 deployment
• Develop systems that enable rapid experimentation, testing, and deployment of AI models
• Create 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 preprocessing
  • Model training and validation
  • Deployment and versioning
  • Monitoring and performance tracking
• Implement best practices for CI/CD for machine learning systems
• Ensure reproducibility, reliability, and traceability of models

Automation & Efficiency

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

Data Pipeline & Integration

• Develop and maintain robust data pipelines and feature stores
• Ensure high-quality, scalable data flows for training and inference
• Integrate 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 efficiency
• Work with containerization and orchestration tools (Docker, Kubernetes)

Monitoring, Observability & Reliability

• Implement monitoring systems for:
• • Model performance and drift
  • Data quality and pipeline health
  • System reliability and uptime
• Build 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 models
• Work closely with engineering teams to integrate ML systems into production environments
• Support teams in adopting AI and automation capabilities effectively

Governance & Security

• Ensure infrastructure meets security, privacy, and compliance requirements
• Support responsible AI practices through:
• • Model versioning and auditability
  • Data governance and access controls

Qualifications

Experience

• 3-5 yrs ML platform engineering for infrastructure, containerization, model serving, monitoring, drift detection, automated retraining pipelines
• Experience building and maintaining production-grade ML systems
• Experience 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 architecture
• Experience building feature stores, model registries, and data pipelines
• Ability to design systems for performance, reliability, and maintainability

AI & Automation Mindset

• Passion for building systems that enable AI at scale and drive automation
• Focus on improving efficiency and reducing manual operational work
• Interest in emerging AI technologies and infrastructure trends

Communication & Collaboration

• Strong ability to work across technical and non-technical teams
• Ability to explain infrastructure and system design decisions clearly
• Collaborative mindset with a focus on team enablement and impact

Education

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

Key Success Metrics

• Deployment speed and reliability of ML models in production
• Reduction in manual effort through automation of ML workflows
• System scalability, uptime, and performance
• Adoption of ML infrastructure and tools across teams
• Efficiency gains in model development and deployment cycles