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Autodesk

Machine Learning Operations Developer, AI/ML Platfor

Toronto, ON, Canada
Montreal, QC, Canada
ON, Canada
AMER - Canada - Ontario - Toronto - University Ave, Canada

For Canada based roles, we expect a starting base salary between $99,000 and $145,200. · Full-time

Apply About employer

Applications open in a new tab.

Job details

Pay: For Canada based roles, we expect a starting base salary between $99,000 and $145,200.
Job type: Full-time
Experience: 2+ years of hands-on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments
Posting language: English · French
Posted: Oct 9, 2026

Original job description

English section of this bilingual listing.

Job Requisition ID #

26WD101494

Machine Learning Operations Developer, Inference, AI/ML Platform

L'affichage de poste en français suivra / The French job posting follows

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

______________________________________________________________________

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $99,000 and $145,200. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

In-Person Onboarding and Identity Verification

This role may require in-person onboarding and/or in-person ID verification.

Read the full bilingual original

Job Requisition ID #

26WD101494

Machine Learning Operations Developer, Inference, AI/ML Platform

L'affichage de poste en français suivra / The French job posting follows

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

______________________________________________________________________

Développeur MLOps (Machine Learning Operations), Inférence, Plateforme IA/ML

Aperçu du poste Autodesk, chef de file mondial dans le domaine des logiciels de conception 3D, d’ingénierie, de fabrication et de divertissement, est à la recherche d’un développeur MLOps pour se joindre à notre équipe de la plateforme IA/ML.

Ce poste contribuera à assurer la mise en œuvre sans heurts des modèles d’apprentissage automatique ainsi que l’efficacité globale de notre plateforme d’IA/ML de nouvelle génération, utilisée dans le développement de solutions d’apprentissage automatique et d’IA générative qui alimentent la suite de produits et de services d’Autodesk. Vous collaborerez avec les équipes de recherche et d’ingénierie de produit issues de divers domaines, notamment la conception, la construction, la fabrication, ainsi que les médias et le divertissement, afin de soutenir les opérations de la plateforme.

Ce poste est hybride (Toronto ou Montréal).

Responsabilités

Contribuer à l’excellence opérationnelle de notre plateforme d’IA/ML en mettant en œuvre et en optimisant les pratiques MLOps
Développer et maintenir des pipelines de déploiement automatisés pour les modèles d’apprentissage automatique, afin de faciliter une transition sans heurts du développement à la production
Collaborer avec des équipes interfonctionnelles pour concevoir, mettre en œuvre et maintenir une infrastructure évolutive pour l’entraînement des modèles, l’inférence et le traitement des données
Contribuer à l’élaboration et à la maintenance de systèmes de surveillance et de journalisation afin de suivre la performance des modèles, l’état du système et l’efficacité globale de la plateforme
Travailler en étroite collaboration avec les développeurs de données pour garantir l’efficacité des pipelines de données destinés à l’entraînement et à la validation des modèles
Mettre en œuvre des systèmes de contrôle de version pour les modèles d’apprentissage automatique et contribuer aux pratiques de gouvernance des modèles
Contribuer à la mise en œuvre de pratiques rigoureuses de gouvernance des modèles, de systèmes de contrôle de version et au respect des normes de conformité. Soutenir la protection des données et les considérations éthiques, afin de renforcer la confiance dans nos solutions d’IA et d’apprentissage automatique
Mettre en œuvre les meilleures pratiques de sécurité et les normes de conformité dans tous les aspects du MLOps, en garantissant la confidentialité des données et la sécurité de la plateforme
Identifier les possibilités d’automatisation et d’optimisation des processus, et mettre en œuvre des stratégies visant à améliorer l’ensemble du cycle de vie du MLOps
Participer à l’identification et à la résolution des problèmes opérationnels, en contribuant à la gestion des incidents et à la reprise des systèmes

Qualifications Minimales

Baccalauréat ou maîtrise en informatique ou dans un domaine connexe
Au moins deux ans d’expérience pratique en DevOps et MLOps, avec un accent sur le déploiement et la gestion de modèles d’apprentissage automatique dans des environnements de production
Expérience de la mise en œuvre de pratiques d’« infrastructure en tant que code » (Infrastructure as Code) à l’aide d’outils tels que Terraform ou Ansible
Expérience des technologies de conteneurisation (Docker, Kubernetes) pour l’orchestration et la mise à l’échelle des charges de travail d’apprentissage automatique
Expérience dans la mise en place et la maintenance de pipelines d’intégration continue et de déploiement continu (CI/CD) pour des projets d’apprentissage automatique
Expérience en création de scripts en Python, Bash ou dans des langages similaires pour l’automatisation des processus opérationnels
Connaissance des outils de surveillance et de journalisation (p. ex. Prometheus, Grafana, ELK Stack) pour le suivi de la performance des systèmes et des modèles
Compréhension des pratiques exemplaires en matière de sécurité dans le domaine des MLOps, y compris le chiffrement des données, les contrôles d’accès et les normes de conformité
Excellentes aptitudes à la collaboration et à la communication, capacité à travailler efficacement au sein d’équipes interfonctionnelles comprenant des développeurs de données, des développeurs de logiciels et des chercheurs
Capacité à diagnostiquer et à résoudre les problèmes opérationnels en temps opportun

Qualifications Souhaitées

Expérience avec les plateformes en nuage, en particulier AWS ou Azure, pour le déploiement et la gestion d’infrastructures d’apprentissage automatique
Connaissance des bases de données et des solutions de stockage de données couramment utilisées en MLOps, telles que SQL, NoSQL ou les lacs de données
Connaissance des frameworks d’apprentissage automatique courants (TensorFlow, PyTorch) et de leur intégration dans les processus MLOps
Expérience préalable avec des outils de collaboration tels que Git pour le contrôle de version et Jira pour la gestion de projet
Connaissance des méthodologies de développement Agile et capacité à travailler dans un environnement itératif et collaboratif

Learn More

About Autodesk

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

In-Person Onboarding and Identity Verification

This role may require in-person onboarding and/or in-person ID verification.

Apply About employer

More about this job

Responsibilities

The MLOps Developer will contribute to the operational excellence of the AI/ML platform by developing automated deployment pipelines and maintaining scalable infrastructure. They will also collaborate with cross-functional teams to implement monitoring, logging, and model governance practices to ensure platform efficiency and security.

Requirements

Candidates must have a BS or MS in Computer Science or a related field and at least 2 years of hands-on experience in DevOps and MLOps. Proficiency in containerization, CI/CD pipelines, Infrastructure as Code, and scripting languages like Python or Bash is required.

Skills

MLOps
DevOps
Python
Bash
Terraform
Ansible
Docker
Kubernetes
CI/CD
Prometheus
Grafana
ELK Stack
Machine learning
Infrastructure as Code
Model governance
Data pipelines

Benefits

Annual cash bonuses
Stock grants

Visa sponsorship

Not detected in the job text

Keywords

MLOps
DevOps
AI/ML Platform
Generative AI
Infrastructure as Code
Terraform
Ansible
Docker
Kubernetes
CI/CD
Python
Bash
Prometheus
Grafana
ELK Stack
Model governance
Data pipelines
AWS
Azure
SQL
NoSQL
TensorFlow
PyTorch
Git
Jira
Agile
Machine learning
Inference
Data processing
Compliance

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