Senior Machine Learning Operations Developer, Inference, AI/ML Platform

Autodesk

. Drive operational excellence of Autodesk’s AI/ML Platform by implementing and optimizing MLOps practices .

Posted 10/6/2026full-timeToronto • CanadaSenior💰 CA$123,000 - CA$180,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in MLOps practices, including automated deployment pipelines, model governance, and operational excellence. Proficient in Infrastructure as Code, with strong collaboration skills to work effectively with cross-functional teams.

Highest-signal resume keywords

MLOps PracticesInfrastructure As CodeDockerKubernetesCI/CD Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ModelsScripting SkillsData PipelinesModel Version ControlMonitoring SystemsData PrivacyIncident ResponseProcess AutomationData EncryptionAccess Controls

Soft Skills

CollaborationCommunicationTroubleshooting

Tools & Technologies

TerraformAnsiblePrometheusGrafanaELK StackAWSAzureSQLNoSQLGit

Industry Keywords

Operational ExcellenceModel TrainingModel InferenceCompliance StandardsAgile Methodologies

Tech Stack

Tools & technologies

AnsibleAWSAzureDockerGrafanaKubernetesNoSQLPrometheusPythonPyTorchSQLTensorflowTerraform

About the role

Key responsibilities & impact

Drive operational excellence of Autodesk’s AI/ML Platform by implementing and optimizing MLOps practices
Design and implement automated deployment pipelines for machine learning models
Collaborate with cross-functional teams to build and maintain scalable infrastructure for model training, inference, and data processing
Develop and maintain monitoring and logging systems for model performance, system health, and platform efficiency
Work with data developers on data pipelines for model training and validation
Implement model version control and contribute to model governance practices
Uphold data privacy, ethical considerations, security best practices, and compliance standards
Identify process automation and optimization opportunities across the MLOps lifecycle
Identify and resolve operational issues, contributing to incident response and system recovery

Requirements

What you’ll need

BS or MS in Computer Science, or related field
5+ years of hands-on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments
Proficiency in Infrastructure as Code using Terraform or Ansible
Strong expertise in Docker and Kubernetes
Experience setting up and managing CI/CD pipelines for machine learning projects
Strong scripting skills in Python, Bash, or similar languages
Familiarity with Prometheus, Grafana, or ELK Stack
Understanding of MLOps security best practices, including data encryption, access controls, and compliance standards
Excellent collaboration and communication skills with cross-functional teams
Proven ability to troubleshoot and resolve complex operational issues
Preferred: experience with AWS or Azure
Preferred: familiarity with SQL, NoSQL, or data lakes
Preferred: exposure to TensorFlow or PyTorch
Preferred: experience with Git and Jira
Preferred: familiarity with Agile methodologies

Benefits

Comp & perks

Annual cash bonuses
Commissions for sales roles
Stock grants
Comprehensive benefits package
In-person onboarding and/or in-person ID verification may be required