ML Deployment Engineer - SickKids AI (SKAI) Service

Dedicated exclusively to children and their families, The Hospital for Sick Children (SickKids) is one of the largest and most respected paediatric healthcare centres in the world. As innovators in child health, we lead and partner to improve the health of children through the integration of healthcare, leading‑edge research and education. Our reputation would not have been built – nor could it be maintained – without the skills, knowledge and experience of the extraordinary people who come to work here every day. SickKids is committed to ongoing learning and development, and features a caring and supportive work environment that combines exceptionally high standards of practice.

When you join SickKids, you become part of our community. We share a commitment and determination to fulfill our vision of Healthier Children. A Better World.

Don’t miss out on the opportunity to work alongside the world’s best in paediatric healthcare.

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.

Responsibilities

• Design, deploy, and maintain hybrid infrastructure spanning on‑premises servers and cloud services
• Productionize AI/ML models developed by data scientists, building robust, scalable, and monitored deployment pipelines
• Build and maintain CI/CD (Continuous Integration / Continuous Deployment) pipelines for automated testing, integration, and deployment of AI/ML systems and infrastructure‑as‑code
• Manage and maintain containerized workloads using Docker and related orchestration tooling
• Administer and optimize relational databases (such as MariaDB) and data lake environments to support AI/ML workflows
• Develop and maintain data pipelines that move and transform data across on‑prem and cloud environments
• Monitor deployed AI/ML systems in production, ensuring reliability, performance, and observability
• Collaborate closely with data scientists and software engineers to accelerate the path from prototype to production
• Contribute to version control best practices and repository management using Git
• Identify and resolve infrastructure bottlenecks, system failures, and performance issues across the stack
• Work with some of the best clinicians, data scientists, and software engineers in the world

Qualifications

• Background in computer science, systems engineering, or a related field with:
• Bachelor’s degree or equivalent with a minimum of 3 years of relevant experience, OR
• Diploma or college certification with a minimum of 5 years of relevant hands‑on experience
• Demonstrated experience managing and operating both on‑prem Linux server environments and cloud services
• Proficiency in Python and shell scripting for automation and systems tooling
• Hands‑on experience with Docker and containerization, including building, deploying, and troubleshooting containerized applications
• Experience designing and maintaining CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, GitLab CI, or similar)
• Proficiency with Git for version control and collaborative development workflows
• Experience administering relational databases, with exposure to MariaDB or similar (MySQL, PostgreSQL)
• Familiarity with data lake concepts and technologies, including structured and unstructured data storage at scale
• Understanding of ML model lifecycle management, including model serving, versioning, and monitoring in production
• Excellent problem‑solving skills and a systematic approach to debugging complex, distributed systems
• Strong verbal and written communication skills with the ability to work collaboratively across technical and clinical stakeholders
• Excellent project and time management skills with strong attention to detail
• Demonstrated commitment to advancing equity, diversity, and inclusion objectives

Bonus Skills

• Experience working in a healthcare or regulated environment with strict data governance and security requirements
• Familiarity with MLflow or similar ML experiment tracking and model registry platforms
• 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
• Knowledge of infrastructure‑as‑code tools (e.g., Terraform, Bicep, Ansible)
• Exposure to data engineering practices, including ETL/ELT (Extract, Transform, Load / Extract, Load, Transform) pipeline development
• Familiarity with monitoring and observability tooling (e.g., Prometheus, Grafana, Azure Monitor)
• Experience with Electronic Health Record (EHR) data or other clinical data systems

Benefits

• 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.
• A focus on employee wellness with our new Staff Health and Well‑being Strategy. Self‑care helps us support others.
• A hospital that welcomes and focuses on Equity, Diversity, and Inclusion.
• 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.

Employment type: Hybrid 1-year Contract (with renewal option).

SickKids is committed to championing equity, diversity and inclusion in all that we do, fostering an intentionally inclusive and culturally safe environment that reflects the diversity of the patients, families and communities we serve. Learn more about workplace inclusion.

If you require accommodation during the application process, please reach out to our aSKHR team. SickKids can provide access and inclusion supports to eligible candidates to support their full engagement during the interview and selection process as well as to ensure candidates are able to perform their duties once successfully hired. If you are invited for an interview and require accommodation, please let us know at the time of your invitation to interview. Information received related to access, inclusion or accommodation will be addressed confidentially.

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