Machine Learning Operations Developer in Toronto

Position Overview

Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking an MLOps Developer to join our AI/ML Platform team. This role will contribute to ensuring the smooth operationalization of machine learning models and the overall efficiency of our next-generation AI/ML platform used in the development of machine learning and generative AI solutions powering Autodesk’s suite of products and services. You will collaborate with research and product engineering teams from various domains including design, construction, manufacturing, and media & entertainment to support platform operations. The role is hybrid Toronto or Montreal.

Responsibilities

• Contribute to the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices.
• Develop and maintain automated deployment pipelines for machine learning models, supporting seamless transitions from development to production.
• Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, and data processing.
• Help develop and maintain monitoring and logging systems to track model performance, system health, and overall platform efficiency.
• Work closely with data developers to ensure efficient data pipelines for model training and validation.
• Implement version control systems for machine learning models and contribute to model governance practices.
• Contribute to the implementation of robust model governance practices, version control systems, and adherence to compliance standards.
• Support data privacy and ethical considerations, fostering trust in our AI/ML solutions.
• Implement security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform security.
• Identify opportunities for process automation and optimization, and implement strategies to enhance the overall MLOps lifecycle.
• Participate in identifying and resolving operational issues, contributing to incident response and system recovery.

Minimum Qualifications

• BS or MS in Computer Science, or related field.
• 2+ years of hands‑on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments.
• Experience implementing Infrastructure as Code practices using tools such as Terraform or Ansible.
• Experience with containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads.
• Experience setting up and maintaining Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects.
• Experience scripting in Python, Bash, or similar languages for automating operational processes.
• Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performance.
• Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards.
• Excellent collaboration and communication skills, working effectively with cross-functional teams including data developers, software developers, and researchers.
• Ability to troubleshoot and resolve operational issues in a timely manner.

Preferred Qualifications

• Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructure.
• Familiarity with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes.
• Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes.
• Previous experience with collaboration tools like Git for version control and Jira for project management.
• Familiarity with Agile development methodologies and working in an iterative, collaborative environment.