Machine Learning (ML) Platform Engineer

PureFacts Financial Solutions•Toronto, ON

About The Position

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.

Requirements

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

Nice To Haves

Experience in SaaS, fintech, or data-driven environments is preferred
Advanced degree is a plus but not required

Responsibilities

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
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
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
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
Design and manage infrastructure on cloud platforms (Azure-based)
Optimize for scalability, performance, and cost efficiency
Work with containerization and orchestration tools (Docker, Kubernetes)
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
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
Ensure infrastructure meets security, privacy, and compliance requirements
Support responsible AI practices through: Model versioning and auditability, Data governance and access controls