Senior Machine Learning Operations Developer: AI/ML Platform

Job Requisition ID: 26WD98671

Location: Canada – Toronto, Montreal, Vancouver or Remote Canada

Position Overview

Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, seeks a skilled MLOps Engineer to join our AI/ML Platform team. This role is pivotal in ensuring the smooth operationalization of machine learning models and enhancing the overall efficiency of our next-generation AI/ML platform used to develop machine learning and generative AI solutions powering Autodesk’s suite of products and services. You will collaborate with research and product engineering across design, construction, manufacturing, and media & entertainment to support platform operations.

Responsibilities

• Drive operational excellence of the AI/ML platform by implementing and optimizing MLOps practices.
• Design and implement automated deployment pipelines for machine learning models, ensuring seamless transition from development to production.
• Collaborate with cross‑functional teams to design, implement, and maintain scalable infrastructure for model training, inference, and data processing.
• Develop and maintain robust monitoring and logging systems to track model performance, system health, and overall platform efficiency.
• Work closely with data engineers 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 robust model governance practices, ensuring compliance with data privacy and ethical standards to foster trust in AI/ML solutions.
• Enforce security best practices and compliance standards across all MLOps activities, ensuring data privacy and platform security.
• Identify opportunities for process automation and optimization, implementing strategies to enhance the MLOps lifecycle.
• Play a key role in identifying and resolving operational issues, contributing to incident response and system recovery.

Minimum Qualifications

• BS or MS in Computer Science or related field.
• 5+ years of hands‑on experience in DevOps and MLOps, focusing on deploying and managing machine learning models in production environments.
• Proficiency in Infrastructure as Code practices using tools such as Terraform or Ansible.
• Strong expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads.
• Experience setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects.
• Strong scripting skills in Python, Bash, or similar for automating operational processes.
• Familiarity with monitoring and logging tools (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 engineers, software developers, and researchers.
• Proven ability to troubleshoot and resolve complex 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 (SQL, NoSQL, data lakes).
• Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes.
• Experience with collaboration tools such as Git for version control and Jira for project management.
• Familiarity with Agile development methodologies and iterative, collaborative workflows.