Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
About the role
- Cloud Full Stack MLOps Engineer developing cloud applications and MLOps at Ontario Brain Institute. Collaborating with stakeholders to enhance workflows and support data models in production.
Responsibilities
- Design, develop, enhance, and maintain CfA cloud-based web applications software and supporting services.
- Support the productization of CfA applications and services for third-party use, including deployment and operational readiness.
- Implement and support MLOps capabilities, including data pipelines, model packaging, deployment, monitoring, and versioning.
- Support federated learning (FL) data and model deployment workflows and associated web applications, as applicable.
- Provide timely technical support to application users; triage issues, resolve incidents, and coordinate fixes with the development team.
- Develop and maintain technical documentation, including architecture, runbooks, and user guidance.
- Participate in application security audits, access reviews, and incident-response drills; implement remediation actions as needed.
- Work closely with other cloud team members to ensure secure and robust cloud resources are configured and managed with high standards.
Requirements
- Bachelor’s degree in computer science, software engineering, data science, or a related field, or an equivalent combination of education and experience.
- 5+ years of professional experience in software engineering, including delivery of production web applications and services.
- Demonstrated experience developing modern web applications (Python; React and/or Vue.js preferred).
- Proficiency with front-end user interface development and back-end services, including database integration.
- Hands-on experience deploying and operating ML models in production, including versioning and containerization.
- Experience developing data ingestion and processing pipelines (ETL/ELT), including custom data loaders.
- Experience working in cloud environments with CPU/GPU and distributed/grid computing (Microsoft Azure preferred).
- Experience delivering software in an Agile/Scrum environment, with strong collaboration and communication skills.
- Demonstrated experience supporting cloud MLOps deployments and operations for model lifecycle management (e.g., CI/CD for ML, model registries, experiment tracking, and monitoring).
- Experience applying engineering best practices, including source control, code review, automated testing, and CI/CD (GitHub experience preferred).
- Experience with Azure services (e.g., compute, storage, networking, identity) and deploying production workloads to Azure.
- Familiarity with security, privacy, and compliance practices for cloud applications (e.g., secure SDLC, threat modeling, vulnerability management).
- Experience supporting regulated or research environments requiring auditability and traceability.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development opportunities
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