A
Autodesk
Senior Machine Learning Operations Developer (Inference, AI/ML Platform)
Skills
SQL QueriesDockerPythonPyTorchTensorFlowGitGrafanaMachine LearningAIAWSAnsiblePrometheusKubernetesJiraWorkdayELK StackComplianceTerraformAzure
2 months ago
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Important information
Contract type:
Permanent
Location:
Toronto, ON, Canada
Work mode:
Onsite
Published on:
8 August 2026
What they need
- Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a skilled Senior MLOps Developer to join our AI/ML Platform team
- This role is pivotal in 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 from various domains including design, construction, manufacturing, and media & entertainment to to support platform operations
- Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices
- Design and implement automated deployment pipelines for machine learning models, ensuring 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
- Develop and maintain robust 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. Uphold data privacy and ethical considerations, fostering trust in our AI/ML solutions
- Enforce security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform securit
- Identify opportunities for process automation, optimization, and implement strategies to enhance the overall MLOps lifecycle
- Play a key role in identifying and resolving operational issues, contributing to incident response and system recovery- Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects
- BS or MS in Computer Science, or related field
- Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards
- Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performance
- 5+ years of hands-on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments
- Proven ability to troubleshoot and resolve complex operational issues in a timely manner
- Strong expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads
- Proficiency in implementing Infrastructure as Code practices using tools such as Terraform or Ansible
- Strong scripting skills in Python, Bash, or similar languages for automating operational processes
- Excellent collaboration and communication skills, working effectively with cross-functional teams including data developers, software developers, and researchers
- Familiarity with Agile development methodologies and working in an iterative, collaborative environment
- 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 databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes
- Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructure
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