Position Overview Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a skilled MLOps Engineer 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 support platform operations.ResponsibilitiesOperational Efficiency: Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practicesDeployment Automation: Design and implement automated deployment pipelines for machine learning models, ensuring seamless transitions from development to productionScalable Infrastructure: Collaborate with cross‑functional teams to design, implement, and maintain scalable infrastructure for model training, inference, and data processingMonitoring and Logging: Develop and maintain robust monitoring and logging systems to track model performance, system health, and overall platform efficiencyCollaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for model training and validationVersion Control and Model Governance: Implement version control systems for machine learning models and contribute to model governance practicesGovernance and Trust: 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 solutionsSecurity and Compliance: Enforce security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform securityContinuous Improvement: Identify opportunities for process automation, optimisation, and implement strategies to enhance the overall MLOps lifecycleTroubleshooting and Incident Response: Play a key role in identifying and resolving operational issues, contributing to incident response and system recoveryMinimum QualificationsEducational Background: BS or MS in Computer Science, or related fieldMLOps Experience: 3+ years of hands‑on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environmentsInfrastructure as Code (IaC): Proficiency in implementing Infrastructure as Code practices using tools such as Terraform or AnsibleContainerization: Strong expertise in containerisation technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloadsCI/CD: Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projectsScripting and Automation: Strong scripting skills in Python, Bash, or similar languages for automating operational processesMonitoring Tools: Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performanceSecurity Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standardsCollaboration Skills: Excellent collaboration and communication skills, working effectively with cross‑functional teams including data engineers, software developers, and researchersProblem‑solving Skills: Proven ability to troubleshoot and resolve complex operational issues in a timely mannerPreferred QualificationsCloud Experience: Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructureDatabase Knowledge: Familiarity with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakesMachine Learning Frameworks: Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processesCollaboration Tools: Previous experience with collaboration tools like Git for version control and Jira for project managementAgile Methodology: Familiarity with Agile development methodologies and working in an iterative, collaborative environment#J-18808-Ljbffr