Machine Learning (ML) Platform Engineer

About PureFacts Financial Solutions PureFacts is a revenue performance software company serving wealth management, asset management, and asset servicing firms. We help financial institutions protect, optimize, and grow revenue through a connected platform spanning pricing, billing, compensation, reporting, and transparency. By unifying fragmented data and workflows into a trusted revenue foundation, we help clients improve accuracy, strengthen governance, reduce manual effort, and unlock new growth opportunities.

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 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, and 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, and 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 years of 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).

Experience with 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.

Compensation

Pay range: 100,000 - 120,000 CAD per year (Toronto, Canada).

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