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    MLOps Engineer, Fusion Team. Canada - Ontario, Canada - Autodesk

    Autodesk
    Autodesk Ontario, Canada

    2 weeks ago

    Autodesk background
    Full time
    Description

    Position Overview

    The Fusion Machine Learning team is a multi-disciplinary team of engineers and researchers developing AI/ML solutions to some of the biggest problems in 3D design, manufacturing, and mechanical engineering. We are seeking an MLOps/DevOps engineer who is passionate about transforming scaling mature production-ready AI powered applications through optimizing the underlying infrastructure.

    You will collaborate with researchers and engineers to continuously improve the data, train and release pipelines by automating repositories to ensure quality and interoperability with deploy systems and upgrading prototype code to run on large cloud-based ML Learning training infrastructure.

    You will report to the Fusion Platform ML manager and play a critical role in the Autodesk AI strategy.

    You will report to the Sr. Manager, Software Development for ML Research.

    Location: The role is located in Canada.

    Responsibilities

  • Operational Efficiency: Improve the team's efficiency by implementing MLOps and DevOps practices
  • Deployment Automation: Adapt research models to automated deployment pipelines
  • Scalable Development: Collaborate with engineers and researchers to ensure research code can transition to training and inference at scale
  • Monitoring and Logging: Track model performance, system health, and overall platform efficiency
  • Version Control and Model Governance: Mature single-version codebases to iterable, pipeline-deployed and version-controlled solutions
  • Security, Compliance and Governance: Contribute to the implementation of model governance practices, and enforce company-wide security best practices and compliance standards in all aspects of DevOps and MLOps
  • Continuous Improvement: Identify opportunities for process automation, optimization, and implement strategies to enhance the MLOps lifecycle
  • Troubleshooting and Incident Response: Help resolve operational issues, contributing to incident response and system recovery

    • Minimum Qualifications

  • Educational Background: BS in Computer Science or related field, or equivalent experience
  • Cloud Experience: Experience with cloud platforms, such as AWS or Azure, for deploying and managing machine learning infrastructure
  • MLOps / DevOps Experience: 3-5 years of hands-on experience in MLOps / DevOps in a production environment
  • Infrastructure as Code (IaC): Proficiency in implementing Infrastructure as Code practices using tools such as Terraform
  • Containerization: Expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning applications
  • CI/CD: Demonstrated experience setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects
  • Scripting and Automation: Scripting skills in Python, Bash, or similar languages for automating operational processes
  • Security Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards

    • ​ Preferred Qualifications:

  • Machine Learning Frameworks: Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes
  • Monitoring Tools: Familiarity / experience with monitoring and logging tools (e.g., Prometheus, Grafana, or ELK Stack) for tracking model and system performance
  • Agile Methodology: Familiarity / experience with Agile development methodologies and working in an iterative, collaborative environment
  • Database Knowledge: Familiarity / experience with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes
  • Repository and Pipeline Technologies: Familiarity / experience with Git, Jenkins, and YAML

    • #LI-POST

    24WD76061 Ingénieur(e) MLOps, équipe Fusion. Canada

    Présentation du poste

    L'équipe d'apprentissage automatique de Fusion est une équipe pluridisciplinaire d'ingénieurs et de chercheurs qui développent des solutions d'IA/apprentissage automatique pour résoudre certains des plus importants problèmes de la conception 3D, de la fabrication et de l'ingénierie mécanique. Nous recherchons un ingénieur ou une ingénieure MLOps/DevOps passionné(e) par la transformation de la mise à l'échelle d'applications matures alimentées par l'IA et prêtes à la production, grâce à l'optimisation de l'infrastructure sous-jacente.

    Vous collaborerez avec des chercheur(e)s et des ingénieur(e)s pour améliorer en permanence les pipelines de données, de formation et de publication en automatisant les référentiels pour garantir la qualité et l'interopérabilité avec les systèmes de déploiement et en mettant à niveau le code prototype pour l'exécuter sur de grandes infrastructures de formation ML basées sur le nuage.

    Vous relèverez le responsable XML de la plate-forme Fusion et vous jouerez un rôle essentiel dans la stratégie d'intelligence artificielle d'Autodesk.

    Vous relèverez du (de la) responsable du développement logiciel pour la recherche en apprentissage automatique.

    Emplacement : Le rôle est situé au Canada.

    Responsabilités

  • Efficacité opérationnelle : améliorer l'efficacité de l'équipe en mettant en œuvre les pratiques MLOps et DevOps.
  • Automatisation de la répartition : adapter les modèles de recherche aux pipelines de déploiement automatisés
  • Développement évolutif : collaborer avec les ingénieurs et les chercheurs pour vous assurer que le code de recherche peut passer à la formation et à l'inférence à grande échelle
  • Surveillance et journalisation : effectuer le suivi des performances du modèle, de l'état du système et de l'efficacité globale de la plate-forme
  • Contrôle des versions et gouvernance des modèles : mûrir les codes bases en version unique vers des solutions itérables, déployées dans le pipeline et contrôlées par les versions
  • Sécurité, conformité et gouvernance : contribuer à la mise en œuvre des pratiques de gouvernance des modèles et appliquer les meilleures pratiques de sécurité et les normes de conformité à l'échelle de l'entreprise dans tous les aspects de DevOps et MLOps.
  • Amélioration continue : identifier les possibilités d'automatisation et d'optimisation des processus et de mise en œuvre de stratégies visant à améliorer le cycle de vie des MLOps.
  • Dépannage et réponse aux incidents : permettre de résoudre les problèmes opérationnels, contribuant ainsi à la réponse aux incidents et à la récupération du système

    • Exigences minimales

  • Formation : baccalauréat en informatique ou dans un domaine connexe, ou expérience équivalente
  • Expérience infonuagique : expérience avec les plates-formes infonuagique, comme AWS ou Azure, pour le déploiement et la gestion d'infrastructures d'apprentissage automatique
  • Expérience MLOps/DevOps : entre 3 ans et 5 ans d'expérience pratique dans les MLOps/DevOps dans un environnement de production
  • Infrastructure sous forme de code (IaC) : maîtrise de la mise en œuvre des pratiques « Infrastructure as Code » à l'aide d'outils tels que Terraform
  • Conteneurisation : expertise dans les technologies de conteneurisation (Docker, Kubernetes) pour l'orchestration et la mise à l'échelle des applications d'apprentissage automatique
  • CI/CD : expérience démontrée dans la configuration et la gestion des pipelines d'intégration continue et de déploiement continu (IC/DC) pour les projets d'apprentissage automatique
  • Script et automatisation : compétences en matière de script en Python, en Bash ou dans d'autres langages similaires pour automatiser les processus opérationnels
  • Reconnaissance de la sécurité : compréhension des meilleures pratiques de sécurité dans les MLOps, notamment le chiffrement des données, les contrôles d'accès et les normes de conformité

    • ​Compétences souhaitées :

  • Structures d'apprentissage automatique : exposition aux structures d'apprentissage automatique les plus utilisées (TensorFlow, PyTorch) et leur intégration aux processus MLOps
  • Outils de surveillance : familiarité / expérience avec les outils de surveillance et de journalisation (par exemple, Prometheus, Grafana, ou ELK Stack) pour le suivi des performances du modèle et du système.
  • Méthodologie agile : familiarisez-vous avec les méthodologies de développement agile et travaillez dans un environnement itératif et collaboratif.
  • Connaissance de la banque de données : familiarité/expérience avec les bases de données et les solutions de stockage de données couramment utilisées dans les programmes MLOps, comme SQL, NoSQL ou les lacs de données
  • Technologies de référentiel et de pipeline : connaissance/expérience de Git, Jenkins et YAML

    • #LI-POST

    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 – our Culture Code is at the core of everything we do. Our values and ways of working help our people thrive and realize their potential, which leads to even better outcomes for our customers.

    When you're an Autodesker, you can be your whole, authentic self and do meaningful work that helps build a better future 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-BC based roles, we expect a starting base salary between $106,500 and $156,200. Offers are based on the candidate's experience and geographic location, and may exceed this range. In addition to base salaries, we also have a significant emphasis on annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

    Diversity & Belonging
    We take pride in cultivating a culture of belonging and an equitable workplace where everyone can thrive. Learn more here:

    Are you an existing contractor or consultant with Autodesk?

    Please search for open jobs and apply internally (not on this external site).

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