SickKids AI is leading a transformative enterprise-wide program to deliver leading edge, data-driven, individualized paediatric health care and operations through AI. Known as SickKids AI Service, or SKAI Service, our team delivers end-to-end AI and machine learning (ML) solutions to high-impact clinical and operational challenges. With a keen focus on sustainable and responsible deployment, you will partner with SickKids' experts to design, build, and maintain the systems infrastructure that powers world-leading AI and ML solutions across all areas of clinical care and hospital operations, enabling us to realize SickKids' vision of Healthier Children. A Better World.
As a ML Deployment Engineer on the SKAI team, you will sit at the intersection of infrastructure and AI/ML engineering, bridging the gap between model development and production. You will be responsible for the reliable operation of our hybrid on-premises and cloud environments, while also contributing hands-on to the productionization and lifecycle management of AI/ML models and data pipelines.
Here's What You'll Get to Do
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Design, deploy, and maintain hybrid infrastructure spanning on-premises servers and cloud services
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Productionize AI/ML models developed by data scientists, building robust, scalable, and monitored deployment pipelines
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Build and maintain CI/CD (Continuous Integration / Continuous Deployment) pipelines for automated testing, integration, and deployment of AI/ML systems and infrastructure-as-code
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Manage and maintain containerized workloads using Docker and related orchestration tooling
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Administer and optimize relational databases (such as MariaDB) and data lake environments to support AI/ML workflows
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Develop and maintain data pipelines that move and transform data across on-prem and cloud environments
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Monitor deployed AI/ML systems in production, ensuring reliability, performance, and observability
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Collaborate closely with data scientists and software engineers to accelerate the path from prototype to production
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Contribute to version control best practices and repository management using Git
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Identify and resolve infrastructure bottlenecks, system failures, and performance issues across the stack
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Work with some of the best clinicians, data scientists, and software engineers in the world
Here's What You'll Need
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Background in computer science, systems engineering, or a related field with:
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Bachelor's degree or equivalent with a minimum of 3 years of relevant experience, OR
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Diploma or college certification with a minimum of 5 years of relevant hands-on experience
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Demonstrated experience managing and operating both on premises Linux server environments and cloud services
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Proficiency in Python and shell scripting for automation and systems tooling
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Hands-on experience with Docker and containerization, including building, deploying, and troubleshooting containerized applications
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Experience designing and maintaining CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, GitLab CI, or similar)
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Proficiency with Git for version control and collaborative development workflows
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Experience administering relational databases, with exposure to MariaDB or similar (MySQL, PostgreSQL)
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Familiarity with data lake concepts and technologies, including structured and unstructured data storage at scale
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Understanding of ML model lifecycle management, including model serving, versioning, and monitoring in production
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Excellent problem-solving skills and a systematic approach to debugging complex, distributed systems
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Strong verbal and written communication skills with the ability to work collaboratively across technical and clinical stakeholders
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Excellent project and time management skills with strong attention to detail
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Demonstrated commitment to advancing equity, diversity, and inclusion objectives
This Will Make You Extra Competitive
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Experience working in a healthcare or regulated environment with strict data governance and security requirements
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Familiarity with MLflow or similar ML experiment tracking and model registry platforms
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Experience with Azure-specific services such as Azure Machine Learning, Azure Data Factory, Azure Blob Storage, and Azure Kubernetes Service (AKS), or equivalent from another cloud provider
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Knowledge of infrastructure-as-code tools (e.g., Terraform, Bicep, Ansible)
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Exposure to data engineering practices, including ETL/ELT (Extract, Transform, Load / Extract, Load, Transform) pipeline development
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Familiarity with monitoring and observability tooling (e.g., Prometheus, Grafana, Azure Monitor)
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Experience with Electronic Health Record (EHR) data or other clinical data systems
Some Exciting Benefits of Working at SickKids
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This position is eligible for employee benefits coverage including but not limited to; health, dental and life insurance. The full benefits package will be discussed at the time of offer.
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A focus on employee wellness with our new Staff Health and Well-being Strategy. Self-care helps us support others.
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A hospital that welcomes and focuses on Equity, Diversity, and Inclusion.
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The opportunity to make an impact. Regardless of your role or professional interest, you will be making a difference at SickKids and contributing to our vision of Healthier Children. A Better World.
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For more on why you'll love working at SickKids, visit our careers site.
Employment type: Hybrid 1-year Contract (with renewal option)
