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Senior Machine Learning Operations Developer: AI/ML Platform 

Autodesk · Toronto ON CAN

Location
Toronto ON CAN
Funding
~$48.8B
Posted
Aug 10, 2026

Autodesk is hiring a Senior Machine Learning Operations Developer: AI/ML Platform  based in Toronto ON CAN. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.

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Role details

Job Requisition ID # 26WD100499 L'affichage de poste en français suivra / The French job posting follows. 26WD100499, Senior Machine Learning Operations Developer: AI/ML Platform Position Overview Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a skilled 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 security 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 process 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 ------------------------------------------------------------------------------------------------------------------ 26WD100499, Développeur senior en opérations d'apprentissage automatique : 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 expérimenté pour rejoindre notre équipe chargée de la plateforme IA/ML. Ce poste est essentiel pour garantir la mise en œuvre sans heurts 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 robustes de surveillance et de journalisation afin de suivre les performances des modèles, l’état de santé 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 modèl

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