About the roleThe
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 doAI 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
QualificationsExperience
- 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
The pay range for this role is:
100,000 - 120,000 CAD per year (Toronto, Canada)
