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Added 2 mins ago
PH
Direct Care Nurse, Minimum and Medium Security Units FPS
Provincial Health Services Authority
Port Coquitlam, BC
- Part-time
…aspects of this position. Under direction of the Clinical Nurse Leader or designate, the Direct Care Nurse provides nursing care delivery and care planning for forensic psychiatric patients…
Added 2 mins ago
PH
Direct Care Nurse, Minimum and Medium Security Units FPS
Provincial Health Services Authority
Port Coquitlam, BC
- Part-time
…aspects of this position. Under direction of the Clinical Nurse Leader or designate, the Direct Care Nurse provides nursing care delivery and care planning for forensic psychiatric patients…
Added 2 mins ago
PH
Direct Care Nurse, Minimum and Medium Security Units FPS
Provincial Health Services Authority
Port Coquitlam, BC
- Part-time
…aspects of this position. Under direction of the Clinical Nurse Leader or designate, the Direct Care Nurse provides nursing care delivery and care planning for forensic psychiatric patients…
Added 2 mins ago
PH
Direct Care Nurse, Minimum and Medium Security Units FPS
Provincial Health Services Authority
Port Coquitlam, BC
- Temporary
…aspects of this position. Under direction of the Clinical Nurse Leader or designate, the Direct Care Nurse provides nursing care delivery and care planning for forensic psychiatric patients…
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OF
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Administrative Assistant 1 [AQ-21000]
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Autodesk

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

Toronto, ON, Canada

For Canada based roles, we expect a starting base salary between $123,000 and $180,400. · Full-time

Apply About employer

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Job details

Pay: For Canada based roles, we expect a starting base salary between $123,000 and $180,400.
Job type: Full-time
Apply by: Nov 4, 2026
Experience: 5+ 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 5, 2026

Original job description

English section of this bilingual listing.

Job Requisition ID #

26WD101354

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

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

Position Overview

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.

Responsibilities

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

Minimum Qualifications

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 implementing 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
Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects
Strong scripting skills 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
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, 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

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 $123,000 and $180,400. 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 #

26WD101354

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

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

Position Overview

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.

Responsibilities

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

Minimum Qualifications

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 implementing 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
Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects
Strong scripting skills 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
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, 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 senior en opérations d'apprentissage automatique, inférence, plateforme IA/ML

Aperçu du poste

Autodesk, leader mondial des logiciels de conception 3D, d'ingénierie, de fabrication et de divertissement, recherche un développeur MLOps senior expérimenté pour rejoindre notre équipe chargée de la plateforme IA/ML. Ce poste est essentiel pour garantir la mise en œuvre fluide des modèles d'apprentissage automatique et l'efficacité globale de notre plateforme 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 services d'Autodesk. Vous collaborerez avec les équipes de recherche et d’ingénierie 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.

Responsabilités

Assurer l’excellence opérationnelle de notre plateforme IA/ML en mettant en œuvre et en optimisant les pratiques MLOps
Concevoir et mettre en œuvre des pipelines de déploiement automatisés pour les modèles d’apprentissage automatique, en garantissant des transitions fluides 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
Développer et maintenir des systèmes de surveillance et de journalisation robustes pour suivre les performances 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 pour l'entraînement et 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 robustes de gouvernance des modèles, de systèmes de contrôle de version et au respect des normes de conformité. Respecter la confidentialité des données et les considérations éthiques, en renforçant la confiance dans nos solutions d'IA/ML
Appliquer les meilleures pratiques de sécurité et les normes de conformité dans tous les aspects des MLOps, en garantissant la confidentialité des données et la sécurité de la plateforme
Identifier les opportunités d'automatisation et d'optimisation des processus, et mettre en œuvre des stratégies visant à améliorer le cycle de vie global des MLOps
Jouer un rôle clé dans l'identification et la résolution des problèmes opérationnels, en contribuant à la gestion des incidents et à la reprise du système

Qualifications Minimales

Licence ou master en informatique ou dans un domaine connexe
Plus de 5 ans d’expérience pratique en DevOps et MLOps, avec une spécialisation dans le déploiement et la gestion de modèles d’apprentissage automatique dans des environnements de production
Maîtrise de la mise en œuvre des pratiques d’« Infrastructure as Code » à l’aide d’outils tels que Terraform ou Ansible
Solide expertise en technologies de conteneurisation (Docker, Kubernetes) pour l’orchestration et la mise à l’échelle des charges de travail d’apprentissage automatique
Expérience avérée dans la mise en place et la gestion de pipelines d’intégration continue et de déploiement continu (CI/CD) pour des projets d’apprentissage automatique
Solides compétences en script en Python, Bash ou dans des langages similaires pour l’automatisation des processus opérationnels
Maîtrise des outils de surveillance et de journalisation (par exemple, Prometheus, Grafana, ELK Stack) pour le suivi des performances du système et des modèles
Maîtrise des meilleures pratiques en matière de sécurité dans le domaine MLOps, notamment le chiffrement des données, les contrôles d’accès et les normes de conformité
Excellentes compétences en matière de collaboration et de communication, capacité à travailler efficacement avec des équipes interfonctionnelles comprenant des développeurs de données, des développeurs logiciels et des chercheurs
Capacité avérée à diagnostiquer et à résoudre rapidement des problèmes opérationnels complexes

Qualifications Souhaitées

Expérience des plateformes cloud, 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 du travail dans un environnement itératif et collaboratif

Learn More

About Autodesk

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 $123,000 and $180,400. 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.

Apply About employer

More about this job

Responsibilities

Operationalize and improve Autodesk’s AI/ML platform by building automated model deployment pipelines and scalable infrastructure for training, inference, and data processing. Maintain monitoring, logging, model governance, security, and compliance practices, while automating workflows and helping resolve operational incidents.

Requirements

Requires a bachelor’s or master’s degree in computer science or a related field and more than five years of hands-on DevOps and MLOps experience deploying and managing production machine learning models. Candidates should have experience with infrastructure as code, containers, CI/CD, scripting, monitoring and logging, security, and troubleshooting, with strong collaboration skills.

Skills

MLOps
DevOps
Infrastructure As Code
Terraform
Ansible
Docker
Kubernetes
CI/CD
Python
Bash
Monitoring And Logging
Prometheus
Grafana
ELK Stack
Model Governance
Incident Response

Benefits

Annual Cash Bonuses
Stock Grants

Visa sponsorship

Not detected in the job text

Keywords

MLOps
DevOps
AI/ML Platform
Machine Learning
Generative AI
Inference
Model Deployment
Infrastructure As Code
Terraform
Ansible
Docker
Kubernetes
CI/CD
Python
Bash
Prometheus
Grafana
ELK Stack
Model Governance
Version Control
Data Pipelines
Model Training
Model Validation
Data Encryption
Access Controls
AWS
Azure
SQL
NoSQL
TensorFlow

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