Senior Machine Learning Operations Developer, Autodesk

• 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

Desirable

• 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
• 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