Senior Machine Learning Operations Developer: AI/ML Platform

TL;DR

Senior Machine Learning Operations Developer: AI/ML Platform (MLOps, DevOps): Building and operating scalable infrastructure and automated deployment pipelines for machine learning and generative AI solutions with an accent on platform reliability, model deployment, monitoring, and governance. Focus on designing CI/CD workflows, orchestrating training and inference workloads, securing data and systems, and resolving complex production incidents.

Location: Toronto, Ontario, Canada. In-person onboarding and/or identity verification may be required.

Salary: CAD 153,000–224,400 annual base salary for Canada-based roles. Offers may exceed this range and may include bonuses, stock grants, and benefits.

Company

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

What you will do

• Drive operational excellence across the AI/ML platform and optimize MLOps practices.
• Design and maintain automated CI/CD pipelines for deploying machine learning models from development to production.
• Build scalable infrastructure for model training, inference, and data processing using containerization and Infrastructure as Code.
• Develop monitoring, logging, model performance tracking, and platform health systems.
• Implement model version control, governance, security, privacy, and compliance practices.
• Investigate operational issues, support incident response, and improve system recovery and automation.

Requirements

• 5+ years of hands-on DevOps and MLOps experience deploying and managing machine learning models in production.
• Proficiency with Infrastructure as Code tools such as Terraform or Ansible.
• Strong experience with Docker and Kubernetes for orchestrating and scaling machine learning workloads.
• Experience managing CI/CD pipelines and scripting with Python, Bash, or similar languages.
• Knowledge of monitoring and logging tools such as Prometheus, Grafana, and ELK Stack.
• Understanding of MLOps security practices, including encryption, access controls, data privacy, and compliance.

Nice to have

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

Culture & Benefits

• Work on AI/ML solutions supporting design, construction, manufacturing, and media and entertainment products.
• Collaborate with research, product engineering, data development, software development, and other cross-functional teams.
• Compensation may include annual cash bonuses, stock grants, and a comprehensive benefits package.
• Commitment to a culture of belonging and meaningful work that supports better-designed and manufactured environments.