Machine Learning Ops Developer (MLOps)

TL;DR

Machine Learning Ops Developer (MLOps): Building and operating scalable AI/ML platform infrastructure for deploying machine learning and generative AI solutions with an accent on deployment automation, model governance, monitoring, and security. Focus on designing CI/CD pipelines, orchestrating production workloads, improving platform reliability, and resolving complex operational incidents.

Location: Toronto, Canada

Salary: CAD 99,000–145,200 base salary annually, with potential bonuses, stock grants, and benefits.

Company

hirify.global develops 3D design, engineering, manufacturing, construction, media, and entertainment software used to transform ideas into real-world products and environments.

What you will do

• Implement and optimize MLOps practices to improve the operational efficiency of the AI/ML platform.
• Design automated deployment pipelines for machine learning models across development and production environments.
• Build and maintain scalable infrastructure for model training, inference, and data processing.
• Develop monitoring and logging systems for model performance, platform health, and system efficiency.
• Collaborate with data engineers, software developers, researchers, and product engineering teams on data pipelines and platform operations.
• Contribute to model governance, security, compliance, incident response, and continuous lifecycle improvement.

Requirements

• 3+ years of hands-on DevOps and MLOps experience deploying and managing machine learning models in production.
• Bachelor’s or master’s degree in Computer Science or a related field.
• Experience with Infrastructure as Code using Terraform or Ansible.
• Strong expertise with Docker, Kubernetes, CI/CD pipelines, and scripting in Python, Bash, or similar languages.
• Experience with monitoring and logging tools such as Prometheus, Grafana, or the ELK Stack.
• Understanding of data encryption, access controls, compliance standards, troubleshooting, and cross-functional collaboration.

Nice to have

• Experience with AWS or Azure cloud platforms.
• Knowledge of SQL, NoSQL databases, data storage solutions, or data lakes.
• Exposure to TensorFlow or PyTorch and their integration into MLOps workflows.
• Experience with Git, Jira, and Agile development methodologies.

Culture & Benefits

• Meaningful work supporting software that enables innovation across design, construction, manufacturing, and entertainment.
• Collaborative environment involving research, product engineering, data engineering, and software development teams.
• Compensation may include annual cash bonuses, stock grants, and a comprehensive benefits package.
• Culture focused on belonging, collaboration, and creating a better world through technology.