Autodesk
Software
MachineLearningOpsDeveloper
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Ops Developer at Autodesk. Skills: MLOps, DevOps, Machine Learning, Infrastructure as Code. Drive operational excellence. Implement MLOps practices”
Industry & Context.
Troubleshoot operational issues; Resolve complex issues
What They're Looking For.
Must Have
BS or MS in Computer Science, 3+ years MLOps experience, 3+ years DevOps experience, Infrastructure as Code proficiency, Containerization expertise, CI/CD experience, Python scripting skills, Bash scripting skills, Familiarity with monitoring tools, Familiarity with logging tools, Security best practices understanding, Collaboration skills, Communication skills, Problem-solving skills
Nice to Have
AWS cloud experience, Azure cloud experience, Database knowledge, Data storage solutions knowledge, Machine learning frameworks exposure, Git proficiency, Jira proficiency, Agile methodology familiarity
What You'll Do.
Drive operational excellence
Implement MLOps practices
Optimize MLOps practices
Design automated deployment pipelines
Implement automated deployment pipelines
Ensure seamless transitions
Design scalable infrastructure
Implement scalable infrastructure
Maintain scalable infrastructure
Develop monitoring systems
Maintain monitoring systems
Develop logging systems
Maintain logging systems
Work with data engineers
Ensure efficient data pipelines
Implement version control systems
Contribute to model governance
Uphold ethical considerations
Foster trust in AI/ML solutions
Enforce security best practices
Enforce compliance standards
Identify process automation opportunities
Identify process optimization opportunities
Implement strategies for MLOps lifecycle
Identify operational issues
Resolve operational issues
Contribute to incident response
Contribute to system recovery
How You'll Work.
Team & Collaboration
Cross-functional teams; Data engineers; Software developers; Researchers
Communication Scope
Collaboration; Communication
Process & Methodology
Agile methodology
Full Job Description
**Job Requisition ID #** 26WD98590 **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 to support platform operations. **Responsibilities** * Operational Efficiency: Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices * Deployment Automation: Design and implement automated deployment pipelines for _machine learning_ models, ensuring seamless transitions from development to production * Scalable Infrastructure: Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for _model_ training, inference, and data processing * Monitoring and Logging: Develop and maintain robust monitoring and logging systems to track _model_ performance, system health, and overall platform efficiency * Collaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for _model_ training and validation * Version Control and _Model_ Governance: Implement version control systems for _machine learning_ models and contribute to _model_ governance practices * Governance 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 solutions * Security and Compliance: Enforce security best practices and compliance standards in all aspects of ML
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